Why This Repo?

Other awesome lists give you paper titles. We give you paper titles + an AI toolkit that makes you faster.

What You Need	Other Lists	This Repo
Browse papers	Static markdown table	Interactive explorer: search, filter, sort
Compare methods	Open 2 papers side by side	10+ dimension auto-comparison
Avoid code bugs	Discover after submission	104 known bug pattern detection
Design experiments	Guess baselines & ablations	Venue-tailored experiment plan
NeRF → 3DGS	Trial-and-error porting	Step-by-step migration guide
CAD → 3DGS	No coverage	40+ method conversion pipeline
Patent filing	Manual from scratch	Auto-generated claims & specs

Live Demo

Try the Interactive Method Explorer →

Search 900 Methods instantly, filter by category, sort by citations, click any method card for details.

Integrations

This repo ships as both AI agent skills and an MCP server, so it works across agent runtimes and MCP clients:

Client	How to use
DSH (DeepSeek Harness)	dsh plugin --profile web add jaccen/Awesome-Gaussian-Skills — the 15 skills + MCP renderer auto-mount via cordis.patch.yml
Claude Code / OpenClaw	skills load directly from skills/ (see CLAUDE.md); MCP server via the config below
Claude Desktop / Cursor / Cline	copy the MCP stdio config from .marketplace/mcp-servers/client-configs.md
Any MCP client	command: node, args: [/path/to/mcp-server/dist/index.js], transport: stdio (21 core tools)

The MCP server exposes 21 tools (import_scene, set_camera, modify_gaussians, render_frame, sculpt_pipeline, SLAT latent editing, …) — see mcp-server/README.md and .marketplace/mcp-servers/mcp-metadata.json.

📖 Online Book: Spatial & Embodied Intelligence (New!)

** NEW (Jul 2026)** —A full open-source technical book, built around 3D Gaussian Splatting as the spine and weaving together spatial intelligence and embodied intelligence into one closed loop: representation → perception → planning → action.

📖 Read the Book →

Core formula (echoing Agent = LLM + Context + Tools):

Embodied Agent = Spatial Representation × Perception × Planning × Action

What's inside — 13 chapters + hands-on prelude & capture SOP, every method name anchored to this repo's real data (900 Methods, 23 categories, 15 skills), zero fabrication:

#	Chapter	Focus
引言	Why this book	Why 3DGS is the key puzzle piece of Physical AI
前	Landing prelude: should you, can you	Tech-selection matrix, eight cases where you should NOT use 3DGS, three reading paths
CH 01	NeRF → 3DGS: A paradigm leap	Explicit vs implicit, the three innovations, the alpha-compositing formula
CH 02	The math & engineering core	Anisotropic Gaussians, differentiable rasterization, adaptive density control, CUDA
操	Data-capture SOP	Device selection, trajectory design, seven exposure hard rules, scale calibration —the first step that decides your quality ceiling
CH 03	From scene to world	Large-scale, dynamic/4D, GS-SLAM, compression & deployment
CH 04	Semantic Gaussians	CLIP/DINO feature distillation, open-vocabulary 3D segmentation
CH 05	Editing · Generation · Asset-ization	Feed-forward reconstruction, SDS generation, animatable assets, PBR relighting
CH 06	Embodied intelligence basics	VLA lineage (RT/π0/GR00T/ReconVLA), simulation, Sim2Real
CH 07	3DGS as robot spatial memory	GS-SLAM, map-as-renderer, three tiers of spatial memory
CH 08	Object-level & articulated understanding	Part-level Gaussians, URDF bridging, the CAD·Mesh·3DGS triangle
CH 09	Agent-driven digital twins	MCP rendering pipeline, gesture interaction, the perception-action loop
CH 10	World models & the future	Six schools of world models, 3DGS×World Model, spatial foundation models, Physical AI
CH 11	Security, provenance & copyright	3DGS watermarking, GaussTrace forensics, adversarial attacks, IP management
CH 12	Capture-to-delivery: the end-to-end pipeline	Ten-stage tool selection & pitfalls, formats & coordinate systems, cost/duration estimates, project failure modes
CH 13	Industry playbooks: six verticals	Museums & tourism, e-commerce, industrial inspection, AD simulation, embodied robots, surveying & mapping
附	Appendix	Datasets, evaluation metrics, delivery-metric conversion & acceptance-report templates
实	Practice index: from book to engineering	15 engineering skills · 34 MCP tools · 900+ method explorer, chapter-to-runnable-resource mapping
后记	Will 3DGS be eaten?	Why explicit representations will be compressed, not consumed

Each chapter ends with hands-on exercises and links back to the repo's method tables, references/, and skills —so reading the book and doing the engineering are one seamless flow.

Highlights you won't find in a paper list:

• The six schools of world models (2026 taxonomy) and where 3DGS sits as the only representation that is simultaneously renderable, differentiable, and editable.
• How GS-World, ManiGaussian, and OrbiSim turn 3DGS into a differentiable simulation engine.
• A three-tier model of robot spatial memory (geometric → appearance → semantic) and where current GS-SLAM actually stands.

Why we wrote it (and how it relates to this repo)

This book is the narrative layer over the repo's data layer. The repo gives you 900 method names, abstracts, and 15 engineering skills —but not the through-line that connects them. The book supplies that through-line: it argues why 3DGS became the bridge between spatial intelligence and embodied intelligence, and walks every chapter back to concrete methods and skills you can use today. Read the book to understand the map; use the repo to ship the territory.

What's New (Oct 2026)

Latest Update (Oct 20, 2026): v0.9.8 — World Models Category Reinforcement (896 → 900) ⭐

Targeted reinforcement of the thinnest category. 4 new arXiv-verified 3DGS-based World Model / Spatial Intelligence methods admitted, taking the library from 896 to 900. The World Models & Spatial Intelligence category grew from 8 → 12 methods: HY-World 2.0, F3DGS (federated multi-agent world modeling), 4DGS-WAM (object-centric world action model), Cloth-Splatting (3DGS cloth state estimation). Two missing arXiv IDs backfilled (Holi-Spatial → 2603.07660, Spatial-TTT → 2603.12255); two entries flagged unverified pending source check (APEIRIA, S²AM3D). GSMem found already in library under Embodied AI & Robotics — not duplicated. See changelog.

Previous Update (Oct 12, 2026): v0.9.7 — Manual Admission: 15 New Methods (881 → 896) ⭐

Manual admission of 15 new arXiv-verified 3DGS methods, taking the library from 881 to 896 (+15, +1.7%). Admissions span 9 categories: Acceleration (H2-Ray, TileSkipper, SplatQ), Feed-Forward (CompactSplat, DeltaSplat, DensiTok), Embodied AI & Robotics (Splat2Act, 2DGS-Planner, NovaSplat), plus OuroWorld (Dynamic & 4D), PAM-ToD (HDR & Relighting), FlyMark (Security), GSCV (Compression & Streaming), OpenSplatGraph (Language & Semantic), PCAsplat (Surface & Rendering). Every admitted method name is self-stated in its own paper's title/abstract (R9 protocol) and arXiv-verified. 12 candidate papers were rejected for lacking a self-stated method name, being benchmark-only, NeRF (not 3DGS), or using 3DGS merely as a tool. All data carriers (methods.json / CSV / methods.html / abstracts.js / reference doc) re-verified at 896, name sets compared entry by entry. No method name was fabricated.

See changelog/2026-10-12.md for the full admission list.

Update (Oct 7, 2026): v0.9.6 — Manual Admission: 9 New Methods (872 → 881) ⭐

Manual admission of 9 new arXiv-verified 3DGS methods, taking the library from 872 to 881 (+9, +1.0%). Admission includes Speedy-Splat (CVPR 2025, precise tile intersection + efficient Hessian-based pruning, 6.71× render speedup) and 8 recent September 2026 papers: AESplat (pose-free feed-forward), AGILE-GS (anchor-guided NBV), OC-GS (irregular turntable), ClearGS (handheld video), ChronoFuseGS (multi-temporal fusion, PG 2026), TangoGS (cross-scale compact), RRTO-CF3DGS (COLMAP-free), Observation-Gram (SH compression). All data carriers (methods.json / CSV / methods.html / abstracts.js / reference doc) re-verified at 881, name sets compared entry by entry. No method name was fabricated.

See changelog/2026-10-07.md for the full admission list.

Update (Sep 21, 2026): v0.9.5 — Daily arXiv Scan: 13 New Methods (859 → 872) ⭐

Routine daily arXiv scan admitted 13 new arXiv-verified 3DGS methods, taking the library from 859 to 872 (+13, +1.5%). Admissions span Surface & Rendering, Feed-Forward, Large-Scale, Compression, Sparse-View, Cross-Domain, Embodied AI & Robotics, Dynamic, and Language & Semantic categories (incl. LiteTex-GS, GrapeSplat, VDGS, CRP-GS, NormLift). 7 candidate papers were rejected for failing the verification protocol — benchmarks without a self-stated method name, descriptive-only paper titles, or 3DGS used merely as a tool. All data carriers (methods.json / CSV / methods.html / abstracts.js / reference doc) re-verified at 872, name sets compared entry by entry. No method name was fabricated; the library's "every entry arXiv-verifiable" guarantee holds.

See changelog/2026-09-21-v3.md for the full admission list.

Earlier updates (v0.9.4 and before)

Latest Update (Sep 21, 2026): v0.9.4 — Multi-Channel Harvest: 700 → 859 Methods + Accuracy Overhaul ⭐

Knowledge base expanded from 700 to 859 verified methods (+159, +22.7%) through a multi-channel arXiv harvest over the last 150 days. Six query families (cs.CV / cs.GR / cs.RO / full-corpus / abs:"3D Gaussian Splatting" / ti:"Gaussian Splatting") returned 395 candidates; after verification 162 were admitted. Also in this release:

• Verification protocol (R1–R6): every admission requires the paper's self-stated method name, reproduced in its own abstract; multi-word names must contain a proper-noun token — this alone rejected 67 descriptions masquerading as method names (Capacity-Controlled, Scene-Level). 114 borderline candidates were read individually; 25 adjudicated in, the rest dropped.
• Accuracy repairs: 57 reference links had a displayed arXiv ID differing from the hyperlinked one (all had the correct ID displayed, wrong link). 13 mis-attributed entries deleted — e.g. Dynamic 3D Gaussians pointed at 2309.13114, which is actually a magnetic spin-glass physics paper, and GS-LRM-v2 pointed at DOF-GS. All now verified against the arXiv API.
• AIGC traces removed from 29 text files; 21 historically corrupted JPEGs restored and re-stripped safely.
• README_CN.md recovered from double-encoding corruption that had mangled every Chinese string.
• Full carrier sync — methods.json / CSV / methods.html / abstracts.js / reference doc all verified at 881, name sets compared entry by entry rather than by count alone.

Adjudication wave (same day). A second pass resolved every name carrying a derived suffix or colliding with another entry: Scaffold-GS+ and GaussianSplatting-SLAM-v2 deleted (no such paper exists), Luminance-GS++ renamed to its self-declared base name, and Spark 2.0's description corrected — it is World Labs' open-source WebGL2 splat renderer, not an NVIDIA robotics simulator. Two independent papers both self-name GeoGS-SLAM (2607.07452 and 2607.11184, disjoint author lists); they are two methods, not a sequel, and are now disambiguated. 862 → 859.

See changelog/2026-09-21.md and changelog/2026-09-21-v2.md for the full audit trail.

Latest update (Sep 20): v0.9.2 —Scene Agent + Mapmost Engineering Wave: 5 New Methods + 6 Skill Enhancements. Knowledge base expanded from 692→ 97 methods. Added 5 scene-generation Agent papers: SceneSmith (ICML 2026 Spotlight, MIT CSAIL —hierarchical designer-critic-orchestrator VLM agents for simulation-ready indoor scenes), Scenethesis (ICLR 2026, NVIDIA —training-free LLM+vision agentic framework), SceneGenAgent (ACL 2025 —LLM coding agent for industrial scenes), SceneConductor (arXiv —multi-agent orchestration from single image), SceneAssistant (arXiv —visual-feedback agent for open-vocabulary scene generation). Injected Mapmost production engineering knowledge into 4 skill files: 3DGS individualization (单体化) in best-practices.md, LOD streaming + seam-fix + adaptive partition training in best-practices.md, 3DGS+3DTiles multi-source fusion in gis-toolchain.md, LUT color grading in tech-stack.md, Mapmost as Digital Twin player with 100km² benchmark in industry-landscape.md. Categories updated: Generation +4, Embodied AI & Robotics +1.

Previous (Sep 20): v0.9.1 —ECCV 2026 Wave: 14 New Methods. Knowledge base expanded from 678→ 92 methods. All 14 new entries arXiv-verified or ECCV-official: Wat3R (underwater 3D geometry without annotations), GeoNVS (geometry-grounded video diffusion with GS-Adapter), Edit3r (instant 3D editing from unposed images), SA-ResGS (first residual learning for 3DGS in next-best-view selection), GaussianLens (on-demand localized high-res reconstruction), Deformable Triangle Splatting (non-convex primitive with per-edge control points), Neural Harmonic Textures (per-primitive harmonic feature decoding), CubicSplat (error-bounded differentiable vector graphics), TetraSDF (analytic isosurface extraction), Heat Kernel Textures (ECCV 2026 Best Paper —geodesic Gaussians), GRF-Recon (long-sequence feed-forward with global ray-field), PriSplat (distractor-free 3DGS via multi-view propagation), Per-View Distractor Filtering (training-free feed-forward artifact removal), GS-Underwater (controlled cross-regime underwater benchmark study). Categories updated: Surface & Rendering +5, Feed-Forward +4, Cross-Domain +2, Robustness +2, Editing +1, Embodied AI & Robotics +1. All data carriers in sync.

Previous (Sep 16): v0.9.0 —Data Accuracy Release. Full-library audit of every entry against the arXiv API: each arXiv ID re-verified reachable and matched to its paper (method name must appear in the title/abstract). 181 entries failed verification and were removed, 56 wrong-ID entries re-linked to their real papers, 5 renamed to the papers' self-declared method names, plus 1 duplicate removed. The count is now honest instead of inflated: 678 methods, 23 categories, 568 arXiv-verified (83%), 130 code links (19%). Per-entry audit evidence: docs/data-accuracy-audit-2026-09-16.md. See changelog/2026-09-16-v5.md.

Previous (Sep 4): v0.8.3 —Dynamic Scene Survey Integration: 23 New Methods. Knowledge base expanded from 801→ 19 verified methods. Added 23 arXiv-verified dynamic scene reconstruction methods from survey paper (连振晗 et al., J CAD & CG, Jan 2026), spanning 3 categories: deformation field (LoopGaussian, CoGS, CD-GS, PGED, GPS-Gaussian, MoDGS, MoDec-GS, SpectroMotion, BARD-GS, GauFRE, ReconDreamer++), sparse-control deformation (SP-GS, Video-3DGS, SplineGS), 4D Gaussian primitive (Real-time 4DGS, PVG, 4D-rotor GS), and per-frame inter-frame transfer (3DGStream, Dual-GS, STC-GS, IGS, GFlow, DynOMo). Dynamic & 4D category: 75→ 7. New references/dynamic-datasets.md (11 datasets) and expanded benchmark-data.md (MS-SSIM/VMAF/FID metrics + 4 performance comparison tables). Experiment planner skill updated with dynamic scene design guide. See changelog/2026-09-04.md.

Previous (Sep 2): v0.8.2 —Daily Update: 11 New Methods. Knowledge base expanded from 790→ 01 verified methods. New additions span 7 categories: BRF-GS (hyperspectral BRDF modeling with 3DGS), SMG (ECCV 2026, semantic motion graph for dynamic GS), VCAR (ACM MM 2026, training-free 3DGS segmentation), RealCAD (real-world image to CAD reconstruction), ObjectSplat (object-level mesh splatting), CapFrame (ECCV 2026, text-instructed viewpoint localization), Amortized Anchor Refinement (continuous-time 4D reconstruction), ATGS (SIGGRAPH 2026, anchored temporal GS for long volumetric video), CausalSplat (hierarchical causal reasoning in 3DGS), VoroTracing (623 FPS differentiable ray tracing), Lucida (Real-to-Sim composable scene modeling). All arXiv IDs verified, data CI passed. See changelog/2026-09-02.md.

Previous (Aug 23): v0.8.1 —Daily Update: 7 New Methods. Knowledge base expanded from 783→ 90 verified methods. New additions span 6 categories: LEGO (ECCV 2026, hierarchical language GS with LLM spatial reasoning), OutLangSplat (UAV outdoor open-vocabulary 3D language GS), ESVR (IEEE VIS 2026, 3D ellipsoid sparse volume rendering with 4 orders of magnitude compression), TRACE-GS (sparse-view 3DGS via privileged geometric conditioning), RORA (single-video-to-articulated-object pipeline with Unreal Engine deployment), OVOW (ECCV 2026, monocular video to instance-level 4D mesh for physics simulation), Super-Gaussian (interactive 3DGS scene editing with VR NLI visualization). All arXiv IDs verified, data CI passed. See changelog/2026-08-23.md.

Previous (Aug 7): v0.8.0 —Platform Upgrade (P0+P1+P2). Knowledge layer: single source of truth (data/methods.json, 783 methods, 23 categories) with data CI; 5 fabricated entries purged; 14 arXiv-verified frontier methods added. Capability layer: true-3DGS render loop (gsplat via HTTP-served PLY), server-authoritative scene persistence, real PLY/SPLAT export, 5 distinct prune strategies, grid-accelerated ray query, runtime arg validation, WS origin allowlist, 21 unit tests. Platform layer: Benchmark arena (bench/), skill orchestration contracts (skills/_contracts/), Router manifest loader (scripts/router_load.py). 13 core MCP tools (all real) + 13 experimental (gated by INCLUDE_EXPERIMENTAL=1). See changelog/2026-08-07.md.

Previous (Jul 26): v0.5.1 —Full Method Audit & 14 New Methods. Now 789+ Methods (775 verified unique baseline + 14 new). Full re-audit across 11 source files; all method counts unified to 789+. New additions: GrainGS (dynamic, 36.98 dB / 435.6 FPS / 4.67 MB), GLAM-SLAM (IROS 2026, outdoor decoupled SLAM), SubSplat (subpixel feed-forward), ATSplat (adaptive 3D tokens, 1136 FPS), 3D-GIMP (3DGS inpainting), LB-Edit (7× lower editing latency), FlexiAvatar (ECCV 2026, visible-body-only optimization), ZeroSplat (ECCV 2026, training-free segmentation), CaT-GS (CVPR 2026, 10× faster rendering), FF-ProCams (projector-camera inverse rendering), i3dgs (SIGGRAPH 2026, large-scale unordered), VIGS-SLAM (ECCV 2026, iPhone real-time), ECoNGS (IEEE VIS 2026, volume visualization), AniGS (scene-level animation via diffusion prior). +MoDE/MoE-GS code link. Previous (Jul 24): v0.5.0 MCP Protocol Implementation. Previous (Jul 23): v0.4.3 ICML 2026 & Material/Provenance Wave —GaussTrace (ICML 2026), GADA (ICML 2026), InvSplat, MGM, DualPhys-GS, StereoGS. v0.4.4 added 3dgs-training-debugger skill (60+ runtime patterns).

Method	Venue	Category	One-Line Innovation
Proxy-GS	CVPR 2026 Oral	Acceleration	Lightweight proxy model for 2.5x speedup with no accuracy loss
Z-Order GS	CVPR 2026 Oral	Feed-Forward	Z-order Morton curve + sparse attention O(N²)→ O(N log N)
3DReflecNet	CVPR 2026 Best Paper Candidate	Cross-Domain	120K+ objects, 48 material combos, 3 failure modes
Flux-GS	ECCV 2026	Acceleration	Flux-based Gaussian splatting for real-time rendering
AnchorSplat	ECCV 2026	Optimization	Anchor-driven splatting with efficient density control
ASSEMCAD	ECCV 2026	CAD	Assembly-aware CAD reconstruction from 3DGS
WildSplat	ECCV 2026	Robustness	In-the-wild scene reconstruction with transient object removal
NoDrift3R	ECCV 2026	SLAM	Drift-free dense 3D reconstruction via point map regression
Axis-Shared Rasterization Accelerator	ISCA 2026	Acceleration	Hardware accelerator with axis-shared tiled rasterization
Prune Wisely	—	Optimization	90% Gaussian pruning via DoG importance criterion
Provable Pruning via Coresets	—	Optimization	Coreset-based provable Gaussian pruning with bounded error
StreamLoD-GS	—	Streaming	LoD-based progressive streaming with view-dependent quality
CADDreamer	CVPR 2025 Highlight	CAD	Text/sketch → CAD B-rep generation
GaussTrace	ICML 2026	Security	3DGS provenance analysis via LLM reasoning for IP forensics
GADA	ICML 2026	Feed-Forward	Geometry-aware deformable aggregation, 2.13× faster FPS
InvSplat	arXiv 2026	Feed-Forward	Inverse feed-forward splatting with intrinsic PBR materials
MGM	arXiv 2026	Relighting	Large material Gaussian model for relightable 3D generation
DualPhys-GS	arXiv 2026	Robustness	Dual physics-guided 3DGS for underwater reconstruction
StereoGS	2026	Acceleration	Energy-efficient hardware stereoscopic GS rendering processor

^{Full changelog: changelog/}

Quick Start

Each skill is a standalone SKILL.md file —copy it to your Agent's skills directory.

3 commands to your first AI-powered 3DGS workflow:

git clone https://github.com/jaccen/Awesome-Gaussian-Skills.git

# Option 1: Claude Code
cp -r Awesome-Gaussian-Skills/skills/* .claude/

# Option 2: Cursor
cp -r Awesome-Gaussian-Skills/skills/* .cursor/rules/

# Option 3: One-Click Install
curl -sSL https://raw.githubusercontent.com/jaccen/Awesome-Gaussian-Skills/main/scripts/setup.sh | bash

Then ask your Agent: "Compare 3DGS and 2DGS rendering formulations"

Knowledge Base (900 Methods, 23 Categories)

Group	Categories	Key Topics
Core Representations	Foundation, Antialiasing, Optimization, Surface/Rendering, Image Rep.	3DGS, 2DGS, Scaffold-GS, Mip-Splatting, GaussianImage
Efficiency & Scale	Compression, Acceleration, Large-Scale, Feed-Forward	Compact-3DGS, BlitzGS, HiGS, VEDAL, VG²GT
Understanding & Semantics	Language/Semantic, Generation, Autonomous Driving	LangSplat, DreamGaussian, StreetNVS
Dynamic & Spatial	Dynamic, HDR, SLAM, Sparse-View, Spatial Intelligence	DSD-GS, WebSpline, GGD-SLAM, Holi-Spatial, Spatial-TTT
Applications	Human/Avatar, Editing, Relighting, CAD, Cross-Domain, Simulation, Robotics, +14 more	AlbedoEdit, KDH-CAD, LEGS, TIDES, 3DEditSafe

Download full database: CSV | Full analysis: references/3dgs-methods-overview.md

Full Category Table (23 categories)

Core Representations

Category	Description	Methods
Foundation (40)	Core 3DGS representations and basic variants	3D Representation Survey, 3DGEER, 3DSGS
Optimization (76)	Training objectives, density control, convergence	AdaGScale, AdpSplit, ArtifactWorld
Surface & Rendering (50)	Surface extraction and rendering-formulation innovation	2D-SuGaR, 3DSS, AmbiSuR

Efficiency & Scale

Category	Description	Methods
Compression & Streaming (43)	Lightweight, mobile, and progressive streaming	CAGS, Clustered Codebook VQ, CodecSplat
Acceleration (10)	Training and inference speedup	3DGS\u00B3, Axis-Shared Rasterization Accelerator, DDF-GS
Large-Scale (20)	City-scale and distributed scene management	BlitzGS, CaT-GS, City-Level 3D Surface
Feed-Forward (67)	Generalizable single-pass reconstruction (incl. foundation models)	AdaptSplat, AnchorSplat, AnyCity

Understanding & Semantics

Category	Description	Methods
Language & Semantic (37)	Open-vocabulary 3D understanding and language fields	3D-GIMP, Consistent Scene Understanding in 3DGS, DGSG-Mind
Generation (27)	Text/condition-driven 3D/4D generation	AniGen, AnySurf, AssetGen
Autonomous Driving (33)	Driving scene reconstruction and simulation	3DGS Safety Evaluation for AD, Asset Harvester, CGGS

Dynamic & Spatial

Category	Description	Methods
Dynamic & 4D (97)	4D Gaussians, temporal deformation, physics-integrated dynamics	3DGS³, AniGS, ClipGStream
HDR & Relighting (27)	HDR capture, relightable and material-aware Gaussians	AlbedoEdit, Ambient-Robust IR, DiffAdapt4DSI
SLAM (40)	Simultaneous localization and mapping	2DGS-SLAM, Anchor3R, Anythingreality
Sparse-View (21)	Few-shot and sparse-view reconstruction	DropAnSH-GS, FrameTwin, GeoQuery
World Models & Spatial Intelligence (8)	3D spatial reasoning, world modeling	ABot-3DWorld 0, APEIRIA, FlashWorld

Applications & Cross-Domain

Category	Description	Methods
Human & Avatar (43)	Animatable human and avatar reconstruction	ArtMesh, CapTalk, COSY
Editing (47)	Interactive and text-guided scene editing	BEA-GS, Capacity-Controlled Stylization, DeSplat
CAD & Reverse Engineering (20)	CAD fitting, B-rep reconstruction, reverse engineering	3DCodeBench, ASSEMCAD, BRepCLIP
Cross-Domain (47)	Medical, underwater, remote sensing and other domains	3DTV, Aes3D, AsyncEvGS
Simulation (11)	Physics simulation and surrogate models	3DThinkVLA, AGILE, ArtiTwinSplat
Embodied AI & Robotics (30)	Grasping, manipulation, navigation, digital twins	3DGS Demo Synthesis (IL), ArtGS, Forecast-GS
Robustness (11)	In-the-wild and degradation-robust reconstruction	3DReflecNet, DelowlightSplat, DualPhys-GS
Security (13)	Watermarking, copyright, forgery detection	3DEditSafe, 4D-GSW, BitC-3DGS

15 AI-Powered Skills

#	Skill	What It Does	Example
1	3dgs-paper-reader	Read any 3DGS paper, extract structured insights	"帮我读一篇 2401.01345 的论文"
2	3dgs-method-compare	Compare variants across 10+ dimensions	"对比 3DGS 和 2DGS 的渲染公式差异"
3	3dgs-code-reviewer	Catch 104 known 3DGS implementation bugs	"审查我的 CUDA 渲染 kernel"
4	3dgs-experiment-planner	Design experiments for CVPR/SIGGRAPH/TVCG	"帮我设计消融实验"
5	nerf-to-3dgs-migrator	Migrate NeRF methods to 3DGS step-by-step	"hash encoding 怎么迁移到 3DGS？"
6	cad-mesh-3dgs	Bridge CAD/Mesh/3DGS — 40+ conversion methods	"3DGS 模型怎么提取高质量 mesh？"
7	cg-paper-writing	Write papers for CVPR/SIGGRAPH/TVCG with adversarial review	"帮我写论文引言"
8	3dgs-visualizer	Publication-quality radar charts, timelines, heatmaps	"画一张 3DGS 方法对比雷达图"
9	3dgs-engineering-guide	Deploy 3DGS from research to production (10 industry tracks)	"怎么部署 3DGS 做自动驾驶仿真？"
10	patent-software-ip	Generate patent applications & software copyrights	"生成专利申请文件"
11	3dgs-spatial-agent	Agent-driven 3D scene reasoning, CAD extraction, editing	"从 3DGS 中提取物体的 CAD 模型"
12	3dgs-mcp-renderer	MCP-controlled Three.js/3DGS rendering bridge	"从上方查看这个场景"
13	3dgs-articulated-reasoner	Articulated object reasoning and digital twin	"打开抽屉"
14	3dgs-compression-deploy	Compress & deploy 3DGS (quantize, prune, VQ, stream, Web/Mobile)	"3DGS 模型怎么压缩到 10MB？"
15	3dgs-training-debugger	Diagnose training failures: OOM, NaN, divergence, artifacts (60+ runtime patterns)	"训练 OOM 了怎么办？"

Works with Claude Code, Cursor, Windsurf, and other AI Agent frameworks.

Visualization Samples

Generated by 3dgs-visualizer —see Test/ for full-resolution files.

Radar Chart	Metrics Bar Chart

Quality vs Speed	Metrics Heatmap

Research Innovation Highlights

Derived from systematic gap analysis across 900 Methods. Target venues: TVCG / CGF / CAD / T-RO / IJCV / ACM TOG.

I-01. Part-Aware Alpha-Compositing for Articulated Objects

Problem: Standard alpha-compositing causes color bleeding at part boundaries of articulated objects. ULF-Loc (CVPR 2026) exposed this feature bias, but no rendering-formulation-level fix exists.

Approach: Extend alpha-compositing with part-aware opacity modulation: C(θ) = Σ Tᵢ · αᵢ · ω_{p(i)}(θ) · cᵢ(θ), where ω penalizes penetration and joint violations, making inter-part penetration regions automatically transparent.

Path: 1) Build on gsplat rasterizer. 2) Add FK layer for articulated objects (URDF). 3) Compute penetration/joint violation via SDF. 4) Train on Articulate-100.

Target: SIGGRAPH / ACM TOG / TVCG

I-02. Geometry-Consistent Flow World Model for Manipulation

Problem: Flow-based world models (RoboFlow4D) predict dense 3D flows but lack geometric consistency —predicted flows can violate object rigidity and physical constraints.

Approach: Couple 3D flow prediction with scene graph constraints: rigidity loss for static objects, articulation loss for joints, support-relation constraints for stacking.

Path: 1) Extend RoboFlow4D. 2) Scene graph parser via OpenMask3D. 3) Geometric regularizer. 4) Train on LIBERO + RoboCasa.

Target: IJCV / T-RO / RSS

I-03. Multi-Scale Occupancy-Gaussian Bidirectional Bridge for Driving

Problem: Occupancy (SparseWorld, DOV) is the driving world model standard; 3DGS provides superior rendering. No differentiable bridge exists between them.

Approach: Bidirectional conversion: Occ→ DGS (learned position+scale predictor from occupancy+semantics) and 3DGS→ Occ (differentiable sparse convolution pooling). Joint backbone for unified prediction+rendering.

Path: 1) Backbone: SparseWorld-TC. 2) Occ→ DGS module. 3) 3DGS→ Occ module. 4) Training: nuScenes, Waymo.

Target: TVCG / T-ITS / CVPR

6 more innovation highlights (I-04 to I-10)

• I-04. Solid Geometry Neural-Symbolic Reasoning: VLM + Z3/SMT formal verifier in iterative refinement loop. Target: Pattern Recognition / AAAI.
• I-05. Embodied Spatial Memory: Hippocampus-inspired 3DGS scene graph + Perceiver compressor + importance-weighted forgetting. Target: T-RO / IJCV.
• I-06. Differentiable Physics Engine: SDF-based contact + differentiable KKT contact solver + Coulomb friction for manipulation. Target: ACM TOG / SIGGRAPH.
• I-06. Tactile-Visual Spatial Fusion: GelSight → contact geometry maps → 3D scene projection → cross-attention fusion. Target: T-RO / ICRA.
• I-08. Panoramic Spatial World Model: Spherical visual panorama + BEV semantic + affordance + spatial relation graph for "imagine then navigate". Target: ECCV / CVPR.
• I-09. Code-as-Spatial-Vocabulary: VLM generates Three.js code → render → extract spatial annotations → fine-tune VLM. Target: CVPR / NeurIPS.
• I-10. Hyperbolic Cross-Modal Distillation: Poincare ball distillation for image→ point-cloud hierarchical feature transfer. Target: T-MM / T-IP.

Roadmap

• v0.1 —Initial release with 6 core skills (Apr 2026)
• v0.2 —3dgs-visualizer + Text2Word demo (May 2026)
• v0.3 —Knowledge base 665->789+ Methods, 25 Categories, 101+ bug patterns, 12 skills (Jun 2026)
• v0.3.6 —Spatial intelligence wave: 680->639+ methods, +10 new methods (FastGS, Holi-Spatial, Spatial-TTT, etc.), Dimension 11, Anthropic standard alignment (Jun 25, 2026)
• v0.3.6 —CVPR 2026 representative papers: 690->639+ methods, +23 verified new methods, all 13 skills updated (Jun 28, 2026)
• v0.4.0 —Router Architecture Expansion: cg-paper-writing + 3dgs-engineering-guide → Router + manifest.yaml + static/; 3dgs-code-reviewer Self-Check Loop; cg-paper-writing Stage Gates; 3 Router skills total (Jul 2, 2026)
• v0.4.1 —ECCV & ISCA 2026 Wave: +Flux-GS, AnchorSplat, ASSEMCAD, WildSplat, NoDrift3R (ECCV 2026), Axis-Shared Rasterization Accelerator (ISCA 2026), Provable Pruning via Coresets; 639+ methods (Jul 9, 2026)
• v0.4.2 —SIGGRAPH & MICCAI 2026 Wave: +DP-Splat, MoE-GS/MoDE (TPAMI 2026), HyperGS, MAC-Splat (ECCV 2026), Track2Map (MICCAI 2026), PEAR (SIGGRAPH 2026), CoSAG, HoloTetSphere (ECCV 2026), SalientGS; 639→ 89+ Methods, +3dgs-compression-deploy skill, 14 skills total (Jul 14, 2026)
• v0.4.3 —ICML 2026 & Material/Provenance Wave: +GaussTrace (ICML 2026), GADA (ICML 2026), InvSplat, MGM, DualPhys-GS, StereoGS; 660→ 89+ Methods, +3 bug patterns (108+ total), MCP roadmap v0.2.3 (Jul 23, 2026)
• v0.4.4 —Training Debugger Skill: +1 skill (3dgs-training-debugger, 60+ runtime patterns, VRAM management, convergence analysis), 14→ 5 skills total (Jul 23, 2026)
• v0.4 —3dgs-spatial-agent enhancements (knowledge-constrained CAD, DDF-GS ray query)
• v0.5.0 —MCP Protocol Implementation: 24-tool MCP server (mcp-server/), Three.js WebSocket renderer, 24-pattern voice intent mapper, headless mode, voice demo (Jul 24, 2026)
• v0.5.1 —Full Method Audit & 14 New Methods: 775 verified unique baseline + 14 new = 789+ methods; all method counts unified across 11 source files; +GrainGS, GLAM-SLAM, SubSplat, ATSplat, 3D-GIMP, LB-Edit, FlexiAvatar, ZeroSplat, CaT-GS, FF-ProCams, i3dgs, VIGS-SLAM, ECoNGS, AniGS (Jul 26, 2026)
• v0.8.0 —Platform Upgrade (P0+P1+P2): single source of truth (data/methods.json, 783 methods, 23 categories, data CI); 5 fabricated entries purged + 14 arXiv-verified frontier methods; true-3DGS render loop (gsplat via HTTP-served PLY); server-authoritative scene persistence; real PLY/SPLAT export; 5 prune strategies; grid-accelerated ray query; runtime arg validation; WS origin allowlist; 21 unit tests + 2 CI workflows; Benchmark arena (bench/); skill orchestration contracts (skills/_contracts/); Router manifest loader (scripts/router_load.py); 13 core MCP tools + 13 experimental (Aug 7, 2026)
• v0.8.1 —Daily Update: 7 new arXiv-verified methods (LEGO, OutLangSplat, ESVR, TRACE-GS, RORA, OVOW, Super-Gaussian); 783→ 90 methods; 6 categories updated; all data carriers in sync (Aug 23, 2026)
• v0.8.2 —Daily Update: 11 new arXiv-verified methods (BRF-GS, SMG, VCAR, RealCAD, ObjectSplat, CapFrame, Amortized Anchor Refinement, ATGS, CausalSplat, VoroTracing, Lucida); 790→ 01 methods; 7 categories updated; all data carriers in sync (Sep 2, 2026)
• v0.8.3 —Dynamic Scene Survey Integration: 23 new arXiv-verified methods from 连振晗 et al. survey (J CAD & CG, Jan 2026); 801→ 19 methods; Dynamic & 4D 75→ 7; +dynamic-datasets.md (11 datasets); +benchmark-data.md metrics (MS-SSIM/VMAF/FID) and 4 performance tables; experiment-planner skill updated (Sep 4, 2026)
• v0.9.0 —Data Accuracy Release: full-library name↔ paper verification against arXiv API; 181 unverifiable entries removed, 56 wrong IDs fixed, 5 renames, 1 dedup; count corrected to 678 (Sep 16, 2026)
• v0.8.4 —Daily Update: 39 new arXiv-verified methods (arXiv window 2026-09-02 ~ 09-16); 819→ 78 methods; 15 categories updated (Compression & Streaming +4, Large-Scale +4, Optimization +4, Robustness +4, Cross-Domain +3, Dynamic & 4D +3, Generation +3, SLAM +3, Acceleration +2, Embodied AI & Robotics +2, Feed-Forward +2, Sparse-View +2, HDR & Relighting +1, Human & Avatar +1, Surface & Rendering +1); pure benchmarks/surveys excluded; validate_knowledge_base.py SyntaxWarning fixed; all data carriers in sync (Sep 16, 2026)
• v0.9.1 —ECCV 2026 Wave: 14 new arXiv-verified methods (Wat3R, GeoNVS, Edit3r, SA-ResGS, GaussianLens, Deformable Triangle Splatting, Neural Harmonic Textures, CubicSplat, TetraSDF, Heat Kernel Textures, GRF-Recon, PriSplat, Per-View Distractor Filtering, GS-Underwater); 678→ 92 methods; 6 categories updated (Surface & Rendering +5, Feed-Forward +4, Cross-Domain +2, Robustness +2, Editing +1, Embodied AI & Robotics +1); all data carriers in sync (Sep 20, 2026)
• v0.9.2 —Scene Agent + Mapmost Engineering Wave: 5 new scene-generation Agent methods (SceneSmith ICML 2026, Scenethesis ICLR 2026, SceneGenAgent ACL 2025, SceneConductor, SceneAssistant); 692→ 97 methods; Generation +4, Embodied AI & Robotics +1; 6 skill file enhancements: 3DGS individualization, LOD streaming + seam-fix, adaptive partition training, 3DGS+3DTiles fusion, LUT color grading, Mapmost 100km² benchmark (Sep 20, 2026)
• v1.0 —CI/CD integration + multi-framework official listings
• v2.0 —Agent-to-Agent collaboration (multi-agent paper discussion)

^{Full version history: changelog/}

Architecture

Awesome-Gaussian-Skills/
├── data/                      # Single source of truth (methods.json, categories.json)
├── skills/                    # 15 AI Agent skills (SKILL.md format)
│   ├── _contracts/            # Inter-skill I/O schemas (paper-insight, comparison-report, experiment-plan)
│   ├── 3dgs-paper-reader/     # Paper reading & summarization
│   ├── 3dgs-method-compare/   # Method comparison engine (Router)
│   ├── 3dgs-code-reviewer/    # Code review (104 bug patterns)
│   ├── 3dgs-experiment-planner/ # Experiment design
│   ├── nerf-to-3dgs-migrator/ # NeRF→3DGS migration
│   ├── cad-mesh-3dgs/         # CAD/Mesh/3DGS bridge
│   ├── cg-paper-writing/      # CG paper writing assistant (Router)
│   ├── 3dgs-visualizer/       # Research visualization
│   ├── 3dgs-engineering-guide/ # Engineering deployment (Router)
│   ├── patent-software-ip/    # Patent & copyright generation
│   ├── 3dgs-spatial-agent/    # Spatial intelligence agent
│   ├── 3dgs-mcp-renderer/     # MCP rendering bridge
│   ├── 3dgs-articulated-reasoner/ # Articulated reasoning & digital twin
│   ├── 3dgs-compression-deploy/  # Compression & deployment
│   └── 3dgs-training-debugger/  # Training failure diagnosis
├── mcp-server/                # MCP server v0.8.0 (13 core + 13 experimental tools, gsplat render loop, HTTP+WS :9842)
├── bench/                     # Benchmark arena (metrics.py, run_eval.py, leaderboard.json)
├── scripts/                   # build_knowledge_base.py, validate_knowledge_base.py, router_load.py, validate_skill_contract.py
├── studio/                    # SplatVerse Studio (bridge + web)
├── docs/                      # GitHub Pages interactive explorer
├── references/                # Knowledge base (900 Methods, 23 Categories)
├── Test/                      # Visualization samples
├── changelog/                 # Version history
└── assets/                    # Project images

Each skill follows the SKILL.md standard, compatible with Claude Code (.claude/), Cursor (.cursor/rules/), Windsurf, and other AI Agent frameworks.

SplatVerse Studio: Short Video Creation

SplatVerse Studio integrates 3D Gaussian Splatting with a short-drama pipeline powered by the Toonflow engine, letting you go from text scripts to 3DGS-rendered video scenes.

Architecture

Toonflow Engine (:10588)                    SplatVerse Studio
+------------------------------+            +------------------------------------------+
| Script -> Assets ->           |   REST     | Bridge (:10590)                           |
| Storyboard -> Video           |<--------->| Project Browser                            |
| Vendor: 3dgs-renderer         |            | Render Studio                              |
|                               |            | MCP Tools (25 tools)                       |
|                               |            | MCP Renderer (:9842)                       |
|                               |            | Pipeline (7 steps)                         |
|                               |            |   |-- Script Adaptation                     |
| MoneyPrinterTurbo (:8081)     |   REST     |   |-- Storyboard                            |
| Online material ->            |<--------->|   |-- Toonflow Sync                         |
| TTS -> FFmpeg -> Video        |            |   |-- TTS Dubbing                           |
| Cross-platform post           |            |   |-- Video Gen                             |
|                               |            |   |-- FFmpeg Compose                        |
| Studio Web (:5173)            |            |   +-- Publish (MPT)                         |
+------------------------------+            +------------------------------------------+

Quick Start

Prerequisites: Node.js ≥18 (Toonflow and MoneyPrinterTurbo are optional).

The one-click launcher starts everything automatically on first run: checks Node.js, creates .env from .env.example if missing, runs npm install if dependencies are missing, builds the MCP Server / Bridge binaries if needed, then starts Toonflow Studio (:10588) (if found), MCP 3D Renderer (:9842), Bridge Server (:10590) and Studio Web (:5173), health-checks every service, and opens your browser.

Option A —Windows (double-click)

Download the project, then double-click start-all.bat in the project root. That's it —all services start automatically and a browser opens at http://localhost:5173.

Option B —npm (any OS)

npm run start:all

Option C —PowerShell (Windows)

powershell -NoProfile -ExecutionPolicy Bypass -File scripts/start-all.ps1

One-time manual step only needed for video generation: set LLM_API_KEY in the generated .env (used for script/storyboard generation).

Toonflow (optional, for the full video pipeline) is auto-detected from ../AI应用/Toonflow-app, ../Toonflow-app, ./Toonflow-app, or TOONFLOW_APP_DIR. MoneyPrinterTurbo (optional, needs Docker) is started only when MPT_ENABLED=true in .env.

Useful flags: -NoToonflow skip Toonflow, -NoMpt skip MPT, -NoBrowser skip auto-opening the browser, -SkipInstall skip npm install.

Minimal (no Toonflow)

If you only need the 3DGS rendering tools without Toonflow:

npm run dev    # MCP + Bridge + Web, no Toonflow

Creating a Short Video: Step by Step

Step 1 —Write a Script in Toonflow

Open the Toonflow web app (typically at http://localhost:10588). Create a project, write a script, and generate storyboards. Each storyboard is a scene with:

• Prompt —text description for image/video generation
• Duration —scene length in seconds
• Track —scene grouping label

Toonflow's pipeline: Text → Script → Assets (roles, scenes, props) → Storyboards → Video

Step 2 —Browse Projects in Studio Web

Open http://localhost:5173 and navigate to Projects. You'll see all Toonflow projects. Click a project to view its storyboards.

Example: Creating a test project via API

# Login to Toonflow
TOKEN=$(curl -s http://localhost:10588/api/login/login \
  -H "Content-Type: application/json" \
  -d '{"username":"admin","password":"admin123"}' | jq -r '.data.token')

# Create a script
curl -s http://localhost:10588/api/script/addScript \
  -H "Authorization: $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"name":"Forest Adventure","content":"# Forest Adventure\n## Scene 1...","projectId":1}'

# Add storyboards
curl -s http://localhost:10588/api/production/storyboard/batchAddStoryboardInfo \
  -H "Authorization: $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "scriptId": 1,
    "projectId": 1,
    "data": [
      {
        "prompt": "A misty forest at dawn, sunlight through trees",
        "videoDesc": "Dawn mist over ancient forest",
        "duration": 5,
        "track": "Scene 1",
        "state": "pending",
        "src": "",
        "shouldGenerateImage": 1,
        "associateAssetsIds": []
      }
    ]
  }'

Step 3 —Render Storyboards as 3DGS Scenes

Two rendering modes in Render Studio (/render):

Mode	What it does	When to use
Direct 3DGS Render	Render a single scene from a text description or .ply file	Quick preview without Toonflow
Batch Render from Toonflow	Render multiple Toonflow storyboards as 3DGS scenes	Full short-video production

For batch rendering, enter the Toonflow Project ID and Storyboard IDs (comma-separated), then click Render Batch. The Bridge fetches storyboards from Toonflow, builds 3DGS scenes via MCP Server, and renders each storyboard frame.

Step 4 —Monitor Render Progress

Render progress streams via SSE (Server-Sent Events). You'll see toast notifications in the top-right corner as each storyboard renders. The Dashboard shows recent render tasks with progress bars.

Step 5 — 3DGS as a Toonflow Vendor (Optional)

To use 3DGS rendering directly inside Toonflow's image/video generation:

# Copy the vendor adapter to Toonflow
cp studio/bridge/vendor/3dgs-renderer.ts ../AI应用/Toonflow-app/data/vendor/

This registers 3DGS as both an image model (single-frame render) and video model (multi-frame animation) in Toonflow's vendor system.

Port Reference

Service	Port	Description
Toonflow Engine	10588	Short-drama creation (external app)
MCP Renderer	9842	WebSocket 3DGS renderer
Bridge Server	10590	REST API + SSE, Toonflow proxy
Studio Web	5173	Vue 3 SPA frontend
MPT API (Sidecar)	8081	MoneyPrinterTurbo FastAPI (optional; 8081 used when host 8080 is taken)
MPT Web UI	8501	MoneyPrinterTurbo Streamlit UI (optional)

Troubleshooting

• "Toonflow engine not running" —Start Toonflow: npm run start:all or manually node data/serve/app.js in the Toonflow directory
• Projects page shows no storyboards —Create a script first in Toonflow; storyboards belong to scripts
• 3DGS vendor not available in Toonflow —Copy studio/bridge/vendor/3dgs-renderer.ts to Toonflow's data/vendor/ directory

MoneyPrinterTurbo (MPT) Integration

MoneyPrinterTurbo is integrated as an optional HTTP API sidecar, extending SplatVerse Studio with online material video generation, additional TTS voices, and cross-platform publishing. Zero intrusion —when MPT_ENABLED=false or MPT_API_URL is unset, the pipeline behaves exactly as before.

What MPT Adds

Capability	Pipeline Step	Role
Extended TTS	Step 4 (TTS)	Fallback when CosyVoice2 → Edge → SAPI all fail; adds Azure / SiliconFlow / ElevenLabs / Gemini voices
Full Video Generation	Step 6 (Compose)	Final fallback when 3DGS / Toonflow / video gen all produce no clips —uses Pexels / Pixabay / Coverr online materials
Cross-Platform Publish	Step 7 (Publish, new)	One-click publish to TikTok / Instagram / YouTube Shorts

Frontend Entry

The Studio Web "Script → Video" page (/pipeline) ships a built-in MPT entry:

• Config panel: a "📖 MoneyPrinterTurbo Integration" group at the bottom of the model config section —enable toggle, service URL, material source, default voice, plus a live connection status (Connected / Not connected). Saving writes the settings into .env.
• Per-task options: a "📖 MoneyPrinterTurbo Fallback" block at the bottom of the input section —check "Video fallback" and "TTS fallback" to automatically degrade to MPT when any main-pipeline step (3DGS / Toonflow / TTS) fails; when MPT is enabled you can also pick an MPT voice and publish platforms (TikTok / YouTube / Instagram).

Note: the bridge auto-loads the project-root .env at startup (dependency-free implementation, see studio/bridge/src/load-env.ts), so config saved from the UI takes effect after the bridge restarts. Launch the bridge from the project root (npm run dev:bridge or scripts/start-dev.ps1) to ensure the root .env is picked up.

Setup

# 1. Configure MPT (fill in at least one material API key)
cp mpt-config.example.toml mpt-config.toml
#    Edit mpt-config.toml —required: pexels.api_key or pixabay.api_key (both free)

# 2. Start MPT container
docker compose -f docker-compose.mpt.yml up -d

# 3. Verify MPT is running
curl http://localhost:8081/api/v1/tasks?page=1&page_size=1

# 4. Enable MPT in Studio .env
#    MPT_ENABLED=true
#    MPT_API_URL=http://localhost:8081

Configuration Reference

Env Variable	Default	Description
MPT_ENABLED	false	Enable/disable MPT integration
MPT_API_URL	(empty)	MPT FastAPI service URL
MPT_MATERIAL_SOURCE	pexels	Material source: pexels / pixabay / coverr / local
MPT_DEFAULT_VOICE	zh-CN-XiaoxiaoNeural	Default TTS voice name

MPT-side settings (API keys for Pexels/Pixabay, TTS providers, LLM) go in mpt-config.toml, not .env. See mpt-config.example.toml for the full template.

API Endpoints

Endpoint	Method	Description
/api/pipeline/mpt/health	GET	Check MPT service availability
/api/pipeline/mpt/bgm	GET	List MPT BGM library
/api/pipeline/tasks/:id/publish	POST	Publish video to platforms ({ platforms: [{ name, title, tags }] })

Creating a Task with MPT Fallback

curl -s http://localhost:10590/api/pipeline/tasks \
  -H "Content-Type: application/json" \
  -d '{
    "text": "In a quiet town, a kitten named HuaHua chats with a butterfly...",
    "title": "HuaHua Adventure",
    "style": "水彩",
    "videoRatio": "16:9",
    "enableTTS": true,
    "enableVideoGen": false,
    "enableMptFallback": true,
    "enableMptTTS": true,
    "mptVoiceName": "zh-CN-XiaoxiaoNeural",
    "publishPlatforms": [
      { "name": "tiktok", "title": "HuaHua Adventure", "tags": ["animation", "cat"] }
    ]
  }'

The 7-step pipeline: Script Adaptation → Storyboard → Toonflow Sync → TTS (MPT fallback) → Video Gen → FFmpeg Compose (MPT fallback) → Publish (MPT)

Contributing

Contributions welcome! See Contributing Guide.

Citation

@misc{awesome-gaussian-skills,
  author = {jaccen},
  title = {Awesome Gaussian Skills: 3D Spatial Intelligence Open-Source Toolbox for 3D Gaussian Splatting Research},
  year = {2026},
  url = {https://github.com/jaccen/Awesome-Gaussian-Skills}
}

Acknowledgments

• 3D Gaussian Splatting —The foundational work
• awesome-3D-gaussian-splatting —The awesome list that inspired this project
• Awesome3DGS/3D-Gaussian-Splatting-Papers —Comprehensive paper collection
• All 3DGS researchers whose papers form our knowledge base

License

Apache-2.0. See LICENSE for details.

Sponsor & Community

If this project helps your research or work, consider supporting us!

Sponsor / 打赏 Buy us a coffee

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