homelab-design/decisions/0062-blender-mcp-3d-avatar-workflow.md

# BlenderMCP for 3D Avatar Creation via Kasm Workstation

* Status: superseded by [ADR-0063](0063-comfyui-3d-avatar-pipeline.md)
* Date: 2026-02-21
* Deciders: Billy
* Technical Story: Enable AI-assisted 3D avatar creation for companions-frontend using BlenderMCP in a Kasm Blender workstation with VS Code, storing assets in S3, serving locally from gravenhollow NFS and remotely via Cloudflare-cached RustFS

## Context and Problem Statement

The companions-frontend serves VRM avatar models for its Three.js-based 3D character rendering (see [ADR-0046](0046-companions-frontend-architecture.md)). Today the avatar library is limited to three models (`Seed-san.vrm`, `Aka.vrm`, `Midori.vrm`) — only one of which actually ships in the repo — and every model must be sourced or hand-sculpted externally.

Creating custom VRM avatars is a manual, time-intensive process: open Blender, sculpt/rig a character, export to VRM, iterate. There is no integration between the AI coding workflow (VS Code / Copilot) and Blender, so context switching between the editor and the 3D tool is constant.

How do we streamline custom 3D avatar creation for companions-frontend with AI assistance, while keeping assets durable and accessible across workstations?

## Decision Drivers

* The existing avatar pipeline is manual and disconnected from the development workflow
* BlenderMCP (v1.5.5, 17k+ GitHub stars) bridges AI assistants to Blender via the Model Context Protocol — enabling prompt-driven 3D modelling, material control, scene manipulation, and code execution inside Blender
* Kasm Workspaces already run in the cluster (`productivity` namespace) and support Docker-in-Docker with volume plugins for persistent storage
* VS Code supports MCP servers natively (GitHub Copilot agent mode), meaning the same editor used for code can drive Blender scene creation
* Custom volume mounts in Kasm map `/s3` to S3-compatible storage via the rclone Docker volume plugin — providing durable, off-node persistence
* Quobyte S3-compatible endpoint with the `kasm` bucket is the existing Kasm storage backend
* VRM models must ultimately land in the companions-frontend `/assets/models/` path at build time or be served from an external URL
* Final production models and animations should live on gravenhollow (all-SSD TrueNAS, dual 10GbE) for fast local serving via NFS
* Remote users accessing companions-chat through Cloudflare Tunnel need a CDN-cached path for multi-MB VRM downloads
* Models are write-once/read-many — ideal for aggressive caching
* gravenhollow already runs RustFS (S3-compatible) — exposing it via Cloudflare Tunnel gives CDN caching without a separate storage tier

## Considered Options

1. **BlenderMCP in Kasm Blender workstation + VS Code MCP client, assets in Quobyte S3 (`kasm` bucket)**
2. **Local Blender + BlenderMCP on a developer laptop**
3. **Hyper3D / Rodin cloud generation only (no Blender)**
4. **Manual Blender workflow (status quo)**

## Decision Outcome

Chosen option: **Option 1 — BlenderMCP in Kasm Blender workstation + VS Code MCP client, assets in Quobyte S3**, because it integrates AI-assisted modelling directly into the existing Kasm + VS Code workflow, stores assets durably in S3, and requires no additional infrastructure beyond what is already deployed.

### Positive Consequences

* AI-assisted 3D modelling — prompt-driven creation, material application, and scene manipulation inside Blender via MCP
* Zero context switching — VS Code agent mode drives Blender commands through the same editor used for code
* Persistent storage — VRM exports written to `/s3` survive session teardown and are available from any Kasm session or CI pipeline
* Existing infrastructure — Kasm agent, DinD, rclone volume plugin, Quobyte S3, gravenhollow NFS, and Cloudflare are all already deployed
* No image rebuild for new models — VRM files live on gravenhollow NFS, mounted read-only into the pod; add a model and update the allowlist
* LAN performance — all-SSD NFS with dual 10GbE delivers VRM files in <100ms
* Remote performance — RustFS exposed through Cloudflare Tunnel with CDN caching at 300+ global PoPs; no separate storage tier needed
* Poly Haven / Hyper3D integration — BlenderMCP supports downloading Poly Haven assets and generating models via Hyper3D Rodin, expanding the asset library
* VRM ecosystem — Blender VRM add-on exports directly to VRM 0.x/1.0 format consumed by `@pixiv/three-vrm` in companions-frontend
* Reproducible — Kasm workspace images are versioned; Blender + add-ons are pre-baked

### Negative Consequences

* BlenderMCP `execute_blender_code` tool runs arbitrary Python in Blender — must trust AI-generated code or review before execution
* Socket-based communication (TCP 9876) between the MCP server and Blender add-on adds a failure mode
* VRM export quality depends on correct rigging/weight painting — AI can scaffold but manual touch-up may still be needed
* Kasm Blender image must be configured with both the BlenderMCP add-on and the VRM add-on pre-installed
* Telemetry is on by default in BlenderMCP — must disable via `DISABLE_TELEMETRY=true` for privacy
* Cache misses from remote users hit gravenhollow via the tunnel — negligible with immutable files and long TTLs

## Architecture

```
┌─────────────────────────────────────────────────────────────────────────┐
│                          Developer Workstation                          │
│                                                                         │
│  ┌──────────────────────────────────┐                                   │
│  │         VS Code (local)          │                                   │
│  │                                  │                                   │
│  │  GitHub Copilot (agent mode)     │                                   │
│  │         │                        │                                   │
│  │         ▼                        │                                   │
│  │  BlenderMCP Server (MCP)         │                                   │
│  │  (uvx blender-mcp)              │                                   │
│  │         │                        │                                   │
│  └─────────┼────────────────────────┘                                   │
│            │ TCP :9876 (JSON over socket)                                │
└────────────┼────────────────────────────────────────────────────────────┘
             │
             ▼
┌─────────────────────────────────────────────────────────────────────────┐
│               Kasm Blender Workstation (browser session)                │
│               kasm.daviestechlabs.io                                    │
│                                                                         │
│  ┌──────────────────────────────────────────────────────┐              │
│  │                   Blender 4.x                        │              │
│  │                                                      │              │
│  │  Add-ons:                                            │              │
│  │   • BlenderMCP (addon.py) — socket server :9876      │              │
│  │   • VRM Add-on for Blender — import/export VRM       │              │
│  │                                                      │              │
│  │  ┌────────────────────────────────────────────────┐  │              │
│  │  │  /s3/blender-avatars/                          │  │              │
│  │  │  ├── projects/          (.blend source files)  │  │              │
│  │  │  ├── exports/           (.vrm exported models) │  │              │
│  │  │  └── textures/          (shared texture lib)   │  │              │
│  │  └────────────────────────────────────────────────┘  │              │
│  └──────────────────────────────────────────────────────┘              │
│                          │                                              │
│                    rclone volume                                        │
│                    plugin (S3)                                          │
└──────────────────────────┼──────────────────────────────────────────────┘
                           │
                           ▼
┌─────────────────────────────────────────────────────────────────────────┐
│                    Quobyte S3 Endpoint                                   │
│                    Bucket: kasm                                          │
│                                                                         │
│  kasm/blender-avatars/projects/Companion-A.blend                        │
│  kasm/blender-avatars/exports/Companion-A.vrm                           │
│  kasm/blender-avatars/textures/skin-tone-01.png                         │
└──────────────────────────┬──────────────────────────────────────────────┘
                           │
                    rclone sync (promotion)
                           │
                           ▼
┌─────────────────────────────────────────────────────────────────────────┐
│            gravenhollow.lab.daviestechlabs.io                            │
│            (TrueNAS Scale · All-SSD · Dual 10GbE · 12.2 TB)            │
│                                                                         │
│  NFS: /mnt/gravenhollow/kubernetes/avatar-models/                       │
│  ├── Seed-san.vrm          (default model)                              │
│  ├── Aka.vrm               (Legend tier)                                │
│  ├── Midori.vrm            (Legend tier)                                │
│  ├── Companion-A.vrm       (custom, promoted from Kasm S3)             │
│  └── animations/           (shared animation clips)                     │
│                                                                         │
│  S3 (RustFS): avatar-models bucket                                      │
│  (same data as NFS dir, served via S3 API for Cloudflare Tunnel)       │
└──────────┬─────────────────────────────────┬────────────────────────────┘
           │                                 │
     NFS mount (nfs-fast)              S3 API (RustFS :30292)
     for pod volume                    via Cloudflare Tunnel
           │                                 │
           ▼                                 ▼
┌──────────────────────────┐   ┌──────────────────────────────────────────┐
│  companions-frontend     │   │       Cloudflare Tunnel + CDN            │
│  (Kubernetes pod)        │   │                                          │
│                          │   │  assets.daviestechlabs.io                │
│  /models/ volume mount   │   │    → envoy-external                      │
│  (nfs-fast PVC, RO)     │   │    → avatar-assets-svc (in-cluster)      │
│                          │   │    → gravenhollow RustFS :30292          │
│  Go FileServer:          │   │                                          │
│  /assets/models/ →       │   │  Cloudflare CDN caches at 300+ PoPs     │
│    serves from PVC       │   │  Cache-Control: public, max-age=31536000 │
│                          │   │  (immutable, versioned filenames)        │
└──────────┬───────────────┘   └──────────────────────┬───────────────────┘
           │                                          │
     LAN clients                              Remote clients
  companions-chat.lab...                   companions-chat via
  (envoy-internal, direct)                 Cloudflare Tunnel
           │                                          │
           └──────────────────┬───────────────────────┘
                              ▼
┌─────────────────────────────────────────────────────────────────────────┐
│                         Browser (Three.js)                               │
│  AvatarManager.loadModel('/assets/models/Companion-A.vrm')              │
│                                                                         │
│  LAN:    fetch from companions-frontend pod (NFS-backed, ~10GbE)       │
│  Remote: fetch from assets.daviestechlabs.io (Cloudflare CDN-cached)   │
└─────────────────────────────────────────────────────────────────────────┘
```

## Workflow

### 1. Kasm Workspace Setup

The Kasm Blender workspace image is configured with:

| Component | Version | Purpose |
|-----------|---------|---------|
| Blender | 4.x | 3D modelling and sculpting |
| BlenderMCP add-on (`addon.py`) | 1.5.5 | Socket server for MCP commands |
| VRM Add-on for Blender | latest | Import/export VRM format |
| Python | 3.10+ | Blender scripting runtime |

The Kasm storage mapping mounts `/s3` via the rclone Docker volume plugin to the Quobyte S3 endpoint (`kasm` bucket). The sub-path `blender-avatars/` is used for all 3D asset work.

### 2. VS Code MCP Configuration

Add BlenderMCP as an MCP server in VS Code (`.vscode/mcp.json` or user settings):

```json
{
  "servers": {
    "blender": {
      "command": "uvx",
      "args": ["blender-mcp"],
      "env": {
        "BLENDER_HOST": "localhost",
        "BLENDER_PORT": "9876",
        "DISABLE_TELEMETRY": "true"
      }
    }
  }
}
```

When the Kasm session is accessed remotely, set `BLENDER_HOST` to the Kasm workstation's reachable address.

### 3. Avatar Creation Workflow

1. **Launch** the Kasm Blender workspace via `kasm.daviestechlabs.io`
2. **Enable** the BlenderMCP add-on in Blender → 3D View sidebar → "BlenderMCP" tab → "Connect to Claude"
3. **Open VS Code** with Copilot agent mode and the BlenderMCP MCP server running
4. **Prompt** the AI to create or modify avatars:
   - _"Create a humanoid character with anime-style proportions, blue hair, and a fantasy outfit"_
   - _"Apply a metallic gold material to the armor pieces"_
   - _"Set up the lighting for a character showcase render"_
   - _"Rig this character for VRM export with standard humanoid bones"_
5. **Export** the finished model to VRM via the VRM add-on (or via BlenderMCP `execute_blender_code` calling the VRM export operator)
6. **Save** the `.vrm` to `/s3/blender-avatars/exports/` and the `.blend` source to `/s3/blender-avatars/projects/`
7. **Import** the VRM into companions-frontend — copy to `assets/models/`, update the allowlists in `internal/database/database.go` and `static/js/avatar.js`

### 4. Asset Pipeline (Kasm S3 → gravenhollow → production)

| Stage | Action |
|-------|--------|
| **Create** | AI-assisted modelling + VRM export in Kasm Blender → `/s3/blender-avatars/exports/*.vrm` |
| **Store** | rclone syncs `/s3` to Quobyte S3 `kasm` bucket automatically |
| **Promote** | `rclone copy quobyte:kasm/blender-avatars/exports/Model.vrm gravenhollow-nfs:/avatar-models/` (manual or CI) |
| **Register** | Add model path to `AllowedAvatarModels` in Go and JS allowlists, commit to repo |
| **Deploy** | Flux rolls out updated companions-frontend config; model already available on NFS PVC — no image rebuild needed |
| **CDN** | Model immediately available via `assets.daviestechlabs.io` — Cloudflare Tunnel proxies to RustFS, CDN caches at edge |

### 5. Deployment and Storage Architecture

#### Local Serving (LAN users)

Companions-frontend currently serves VRM models via `http.FileServer(http.Dir("assets"))` from the container filesystem. This bakes models into the image and requires a rebuild to add new avatars.

The new approach mounts avatar models from gravenhollow via an `nfs-fast` PVC:

```yaml
# PersistentVolumeClaim for avatar models
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: avatar-models
  namespace: ai-ml
spec:
  storageClassName: nfs-fast
  accessModes: [ReadOnlyMany]
  resources:
    requests:
      storage: 10Gi
```

The pod mounts this PVC at `/models` and the Go server serves it at `/assets/models/`:

```go
// Replace embedded assets with NFS-backed volume
mux.Handle("/assets/models/", http.StripPrefix("/assets/models/",
    http.FileServer(http.Dir("/models"))))
```

Benefits:
- **No image rebuild** to add/update models — write to gravenhollow NFS, pod sees it immediately (with `actimeo=600` cache, within 10 minutes)
- **All-SSD + dual 10GbE** — VRM files (typically 5–30 MB) load in <100ms on LAN
- **ReadOnlyMany** — multiple replicas can share the same PVC
- Source `.blend` files and textures remain on Quobyte S3 (Kasm bucket) for the creation workflow; only promoted VRM exports land on gravenhollow

#### Remote Serving (Cloudflare-cached RustFS)

Companions-chat is accessed externally via Cloudflare Tunnel → `envoy-internal`. Rather than duplicating assets to a separate storage tier (e.g., Cloudflare R2), gravenhollow's RustFS S3 endpoint is exposed directly through the Cloudflare Tunnel with a dedicated hostname. Cloudflare's CDN automatically caches responses at edge PoPs — since VRM files are immutable with year-long TTLs, virtually all requests are served from cache.

| | |
|---|---|
| **Origin** | gravenhollow RustFS `avatar-models` bucket (`:30292`, same data as NFS dir) |
| **Public hostname** | `assets.daviestechlabs.io` (Cloudflare DNS, orange-clouded) |
| **Tunnel routing** | Cloudflare Tunnel → `envoy-external` → `avatar-assets-svc` → gravenhollow RustFS |
| **CDN caching** | Cloudflare CDN caches at 300+ global PoPs; `Cache-Control: public, max-age=31536000, immutable` |
| **Egress** | Cloudflare-proxied traffic has no bandwidth surcharge |
| **Auth** | Public read (models are not sensitive); RustFS write credentials stay internal |
| **No sync needed** | Single source of truth — NFS and RustFS serve the same data from gravenhollow |

##### In-Cluster Proxy Service

An ExternalName or Endpoints service proxies cluster traffic to gravenhollow's RustFS endpoint so the HTTPRoute can reference it:

```yaml
# Service pointing to gravenhollow RustFS for avatar assets
apiVersion: v1
kind: Service
metadata:
  name: avatar-assets
  namespace: ai-ml
spec:
  type: ExternalName
  externalName: gravenhollow.lab.daviestechlabs.io
  ports:
    - port: 30292
      protocol: TCP
```

##### HTTPRoute (Cloudflare Tunnel → RustFS)

```yaml
apiVersion: gateway.networking.k8s.io/v1
kind: HTTPRoute
metadata:
  name: avatar-assets
  namespace: ai-ml
  annotations:
    external-dns.alpha.kubernetes.io/hostname: assets.daviestechlabs.io
spec:
  hostnames:
    - assets.daviestechlabs.io
  parentRefs:
    - name: envoy-external
      namespace: network
  rules:
    - matches:
        - path:
            type: PathPrefix
            value: /avatar-models/
      backendRefs:
        - name: avatar-assets
          port: 30292
      filters:
        - type: ResponseHeaderModifier
          responseHeaderModifier:
            set:
              - name: Cache-Control
                value: "public, max-age=31536000, immutable"
              - name: Access-Control-Allow-Origin
                value: "https://companions-chat.daviestechlabs.io"
```

Cloudflare Tunnel picks up `assets.daviestechlabs.io` via the existing wildcard ingress rule (`*.daviestechlabs.io → envoy-external`). The CDN caches based on the `Cache-Control` header — after the first request per PoP, all subsequent loads are served from Cloudflare's edge.

##### Client-Side Routing

The frontend detects whether the user is on LAN or remote and routes model fetches accordingly:

```javascript
// avatar.js — model URL resolution
function resolveModelURL(path) {
    // LAN users: serve from the Go server (NFS-backed, same origin)
    // Remote users: serve from Cloudflare-cached RustFS
    const isLAN = location.hostname.endsWith('.lab.daviestechlabs.io');
    if (isLAN) return path; // e.g. /assets/models/Companion-A.vrm
    return `https://assets.daviestechlabs.io/avatar-models/${path.split('/').pop()}`;  
    // → https://assets.daviestechlabs.io/avatar-models/Companion-A.vrm
}
```

Alternatively, the Go server can set the model base URL via a template variable based on the `Host` header, keeping the logic server-side.

#### Versioning Strategy

VRM files are immutable once promoted — updated models get a new filename (e.g., `Companion-A-v2.vrm`) rather than overwriting. This ensures:
- Cloudflare CDN cache never serves stale content
- Rollback is trivial — point the allowlist back to the previous version
- Browser `Cache-Control: immutable` works correctly

#### Storage Tier Summary

| Location | Purpose | Tier | Access |
|----------|---------|------|--------|
| Quobyte S3 (`kasm` bucket) | Working files: `.blend`, textures, WIP exports | Kasm rclone volume | Kasm sessions only |
| gravenhollow NFS (`/avatar-models/`) | Production VRM models + animations | `nfs-fast` PVC (RO) | companions-frontend pod, LAN |
| gravenhollow RustFS S3 (`avatar-models`) | Same data as NFS, exposed to Cloudflare Tunnel for remote users | S3 API via HTTPRoute | Cloudflare CDN-cached, global |

## BlenderMCP Capabilities Used

| MCP Tool | Avatar Workflow Use |
|----------|-------------------|
| `get_scene_info` | Inspect current scene before modifications |
| `create_object` | Scaffold base meshes for characters |
| `modify_object` | Adjust proportions, positions, bone placement |
| `set_material` | Apply skin, hair, clothing materials |
| `execute_blender_code` | Run VRM export scripts, batch operations, custom rigging |
| `get_screenshot` | AI reviews viewport to understand current state |
| `poly_haven_download` | Fetch HDRIs, textures for environment/materials |
| `hyper3d_generate` | Generate base 3D models from text prompts via Hyper3D Rodin |

## Security Considerations

* **Code execution:** BlenderMCP's `execute_blender_code` runs arbitrary Python in Blender. The Kasm session is sandboxed (DinD container with no cluster access), limiting blast radius. Always save before executing AI-generated code.
* **Telemetry:** BlenderMCP collects anonymous telemetry by default. Disabled via `DISABLE_TELEMETRY=true` in the MCP server config.
* **Network:** The TCP socket (port 9876) between the MCP server and Blender add-on is local to the session. If accessed remotely, ensure the connection is tunnelled or restricted.
* **S3 credentials:** rclone volume plugin credentials are managed via Kasm storage mappings and the existing `kasm-agent` ExternalSecret — no new secrets required.
* **RustFS exposure:** The `avatar-models` RustFS bucket is exposed read-only through Cloudflare Tunnel. RustFS write credentials remain internal. The HTTPRoute only routes GET requests to the bucket path — no write operations are reachable externally.
* **Public assets:** Avatar models are public assets (served to any authenticated companions-chat user). No sensitive data in VRM files. CORS restricts to `companions-chat.daviestechlabs.io` origin.
* **Model allowlist:** Even though models are served from NFS/R2, the server-side and client-side allowlists in companions-frontend gate which models users can actually select. Uploading a VRM to gravenhollow does not make it available without a code change.

## Pros and Cons of the Options

### Option 1 — BlenderMCP in Kasm + VS Code + Quobyte S3 + gravenhollow (NFS + RustFS via Cloudflare)

* Good, because AI-assisted modelling reduces manual effort for avatar creation
* Good, because assets persist in S3 across sessions and are accessible from CI
* Good, because no new infrastructure — Kasm, rclone, Quobyte, gravenhollow, Cloudflare Tunnel are all already deployed
* Good, because VS Code MCP integration means one editor for code and 3D work
* Good, because Kasm sandboxes Blender execution away from the cluster
* Good, because NFS-fast serving decouples model assets from container images (no rebuild to add models)
* Good, because RustFS through Cloudflare Tunnel provides CDN caching with zero additional storage tiers — no R2 bucket, no sync CronJob, no extra credentials
* Good, because single source of truth — gravenhollow serves both LAN (NFS) and remote (RustFS → Cloudflare CDN) from the same data
* Good, because immutable versioned filenames enable aggressive caching and trivial rollback
* Good, because models are available to remote users immediately after promotion (no sync delay)
* Bad, because BlenderMCP is a third-party tool with arbitrary code execution
* Bad, because socket communication adds latency for remote Kasm sessions
* Bad, because VRM rigging quality may require manual adjustment after AI scaffolding
* Bad, because cache misses hit gravenhollow via the tunnel (negligible with immutable files + long TTLs)

### Option 2 — Local Blender + BlenderMCP on developer laptop

* Good, because lowest latency (everything local)
* Good, because no Kasm dependency
* Bad, because assets are local — no durable S3 storage without manual sync
* Bad, because Blender + add-ons must be installed on every dev machine
* Bad, because not reproducible across machines

### Option 3 — Hyper3D / Rodin cloud generation only

* Good, because no Blender installation needed
* Good, because fully prompt-driven model generation
* Bad, because limited control over output — no fine-tuning materials, rigging, or proportions
* Bad, because Hyper3D free tier has daily generation limits
* Bad, because generated models require post-processing for VRM compliance (humanoid rig, expressions, visemes)
* Bad, because vendor dependency for a core asset pipeline

### Option 4 — Manual Blender workflow (status quo)

* Good, because full manual control
* Good, because no new tooling
* Bad, because slow — no AI assistance for repetitive modelling tasks
* Bad, because no integration with the development workflow
* Bad, because assets stored ad-hoc with no structured pipeline to companions-frontend

## Links

* Related to [ADR-0046](0046-companions-frontend-architecture.md) (companions-frontend architecture — Three.js + VRM avatars)
* Related to [ADR-0026](0026-storage-strategy.md) (storage strategy — gravenhollow NFS-fast, Quobyte S3, rclone)
* Related to [ADR-0044](0044-dns-and-external-access.md) (DNS and external access — Cloudflare Tunnel, split-horizon)
* Related to [ADR-0049](0049-self-hosted-productivity-suite.md) (Kasm Workspaces)
* Related to [ADR-0059](0059-mac-mini-ray-worker.md) (waterdeep as local AI agent — primary 3D creation workstation with Metal GPU)
* [BlenderMCP GitHub](https://github.com/ahujasid/blender-mcp)
* [VRM Add-on for Blender](https://vrm-addon-for-blender.info/en/)
* [VRM Specification](https://vrm.dev/en/)
* [@pixiv/three-vrm](https://github.com/pixiv/three-vrm) (runtime loader used in companions-frontend)
* [Poly Haven](https://polyhaven.com/) (free 3D assets, HDRIs, textures)
* [Hyper3D Rodin](https://hyper3d.ai/) (AI 3D model generation)
* [Cloudflare Tunnel Docs](https://developers.cloudflare.com/cloudflare-one/connections/connect-networks/)
* [Cloudflare CDN Cache Rules](https://developers.cloudflare.com/cache/)