3dgs-articulated-reaso…
3DGS Articulated Object Reasoning & Digital Twin Agent. Reason about articulated objects in 3DGS scenes: extract part structure,…
MCP protocol integration with 3DGS rendering pipeline: Agent-controlled Three.js/WebGPU rendering, voice-driven scene reconstruction, real-time parameter manipulation, light tracing backend. Use when: MCP rendering, agent-controlled 3DGS, voice-driven reconstruction, real-time
$ npx -y skills add jaccen/Awesome-Gaussian-Skills --skill 3dgs-mcp-renderer --agent claude-codeHow it fires
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/3dgs-mcp-rendererContext preview
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MCP protocol integration with 3DGS rendering pipeline: Agent-controlled Three.js/WebGPU rendering, voice-driven scene reconstruction, real-time parameter manipulation, light tracing backend. Use when: MCP rendering, agent-controlled 3DGS, voice-driven reconstruction, real-time
name: 3dgs-mcp-renderer description: 'MCP protocol integration with 3DGS rendering pipeline: Agent-controlled Three.js/WebGPU rendering, voice-driven scene reconstruction, real-time parameter manipulation, light tracing backend. Use when: MCP rendering, agent-controlled 3DGS, voice-driven reconstruction, real-time 3DGS editing, Three.js 3DGS, WebGPU Gaussian splatting, interactive rendering control, speech-to-3D, light tracing, HiGS accelerated rendering.' license: Apache-2.0 metadata: version: 1.1.0 author: jaccen tags: - mcp - 3dgs - gaussian-splatting - rendering - three.js - webgpu - voice - agent - interactive - spec-first - sculpting - code-first - slat - latent-editing disable-model-invocation: true user-invocable: true
> Representation boundary: read `../../references/slat-unified-representation.md` before claiming learned encoding or conversion capabilities. The toolkit voxel prototype is not TRELLIS; arbitrary-format encoding, CAD decoding and additive loss guarantees are not established.
Prototype specification for integrating MCP (Model Context Protocol) with 3DGS rendering pipelines, enabling AI Agents to directly manipulate Three.js/3DGS rendering parameters and achieve voice-driven 3D scene reconstruction.
┌─────────────┐ ┌─────────────┐ ┌──────────────────┐ ┌──────────────────┐
│ Voice/Text │────▶│ Agent │────▶│ MCP Server │────▶│ 3DGS Renderer │
│ (Whisper/ │ │ (Claude/ │ │ (Node.js/ │ │ (Three.js/ │
│ Prompt) │ │ TeleClaw) │ │ Python) │ │ WebGPU/HiGS/ │
│ │◀────│ │◀────│ │◀────│ DDF-GS) │
└─────────────┘ └─────────────┘ └──────────────────┘ └──────────────────┘
│ │ │
│ Tool calls │ WebSocket/HTTP │ WebGL/WebGPU/
│ (MCP protocol) │ transport │ HiGS/DDF-GS> **Design inspiration**: img2threejs (GitHub: img2threejs/img2threejs) — open-source AI Skill that converts a single image into an interactive Three.js 3D model via a stage-gated sculpting pipeline. We borrow two core principles: (1) **spec-first** — define quality criteria and component hierarchy before any rendering; (2) **stage-gated sculpting** — progressive refinement with acceptance checks at each stage.
The original MCP pipeline was **reactive**: user issues a voice command → agent maps to a tool → render → verify. This works for single-step edits but fails for complex scene construction because:
**The fix**: Introduce a `define_scene_spec` tool that runs *before* any sculpting/editing tools. This produces a machine-readable Object Spec that subsequent tools reference as acceptance criteria.
┌─────────────────────────────────────────────────────────────┐ │ SPEC-FIRST SCULPTING │ │ │ │ ┌──────────────┐ │ │ │ define_scene │ ← Object Spec: component hierarchy, │ │ │ _spec │ material system, quality criteria │ │ └──────┬───────┘ │ │ │ │ │ ▼ │ │ Stage 1: blockout → Bounding boxes, camera framing │ │ │ gate: bbox coverage ≥ spec.target_coverage? │ │ ▼ │ │ Stage 2: structural → Part decomposition, hierarchy │ │ │ gate: part count & nesting matches spec? │ │ ▼ │ │ Stage 3: form → Gaussian density/scale/rotation │ │ │ gate: PSNR estimate ≥ spec.min_psnr? │ │ ▼ │ │ Stage 4: material → PBR/SH assignment per part │ │ │ gate: material count per part matches spec? │ │ ▼ │ │ Stage 5: surface → Normal consistency, thin structures │ │ │ gate: normal consistency score ≥ spec.threshold? │ │ ▼ │ │ Stage 6: lighting → Environment, shadows, AO │ │ gate: render quality score ≥ spec.target_score? │ └─────────────────────────────────────────────────────────────┘
Each stage is an MCP tool call. The agent renders a frame after each stage, evaluates against the gate, and either advances or retries. This mirrors img2threejs's `blockout → structural → form → material → surface → lighting` flow.
For each stage gate, the agent follows this protocol:
1. Execute stage tool (e.g., sculpt_form with parameters) 2. Call render_frame() to get current visual state 3. Call query_scene(query_type="stats") to get quantitative metrics 4. Compare metrics against spec gate criteria 5. If pass → advance to next stage 6. If fail → adjust parameters and retry (max 3 attempts) 7. If 3 failures → report to user with diagnostic info
> **Loaded on demand** — See [mcp-tools-spec.md](references/mcp-tools-spec.md) for the full voice-driven sculpting example (desk scene with 8-step agent pipeline).
> **Design i
900 arXiv-verified 3DGS methods · 15 AI agent skills · MCP server (34 tools) · open ebook — the AI-native entry to 3D Gaussian Splatting research
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