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

BlenderMCP for 3D Avatar Creation via Kasm Workstation

• Status: proposed
• 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 R2 CDN

Context and Problem Statement

The companions-frontend serves VRM avatar models for its Three.js-based 3D character rendering (see ADR-0046). 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-backed path for multi-MB VRM downloads
• Models are write-once/read-many — ideal for aggressive caching

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 — Cloudflare R2 CDN with zero egress fees and 300+ global PoPs for remote users via Cloudflare Tunnel
• 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
• Cloudflare R2 sync adds a CronJob and requires a Cloudflare R2 API token in Vault
• Two-hop promotion path (Quobyte S3 → gravenhollow NFS → Cloudflare R2) adds operational steps

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                                      │
│  (mirror of NFS dir for Cloudflare R2 sync)                             │
└──────────┬─────────────────────────────────┬────────────────────────────┘
           │                                 │
     NFS mount (nfs-fast)              rclone sync (cron)
           │                                 │
           ▼                                 ▼
┌──────────────────────────┐   ┌──────────────────────────────────────────┐
│  companions-frontend     │   │         Cloudflare R2                    │
│  (Kubernetes pod)        │   │    Bucket: avatar-models                 │
│                          │   │                                          │
│  /models/ volume mount   │   │  Custom domain:                          │
│  (nfs-fast PVC, RO)     │   │  assets.daviestechlabs.io/models/        │
│                          │   │                                          │
│  Go FileServer:          │   │  Cache-Control: public, max-age=31536000 │
│  /assets/models/ →       │   │  (immutable, versioned filenames)        │
│    serves from PVC       │   │                                          │
│                          │   │  Free egress (no bandwidth charges)      │
└──────────┬───────────────┘   └──────────────────────┬───────────────────┘
           │                                          │
     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 Cloudflare R2 CDN (cache-hit, global PoPs)         │
└─────────────────────────────────────────────────────────────────────────┘

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):

{
  "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 sync	CronJob rclone sync from gravenhollow RustFS avatar-models bucket → Cloudflare R2 avatar-models bucket

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:

# 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/:

// 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 R2 CDN)

Companions-chat is accessed externally via Cloudflare Tunnel → envoy-internal. Serving multi-MB VRM files through the tunnel works but adds latency and consumes tunnel bandwidth. Cloudflare R2 provides a better path:

Bucket	avatar-models on Cloudflare R2
Custom domain	assets.daviestechlabs.io (Cloudflare DNS, orange-clouded)
Free egress	R2 has zero egress fees — ideal for large binary assets
Cache	Cloudflare CDN caches at 300+ global PoPs; Cache-Control: public, max-age=31536000, immutable
Sync	CronJob in cluster: rclone sync gravenhollow-s3:avatar-models r2:avatar-models --checksum
Auth	Public read (models are not sensitive); write via R2 API token in Vault

R2 Sync CronJob

apiVersion: batch/v1
kind: CronJob
metadata:
  name: avatar-models-r2-sync
  namespace: ai-ml
spec:
  schedule: "0 */6 * * *"    # Every 6 hours
  jobTemplate:
    spec:
      template:
        spec:
          containers:
            - name: sync
              image: rclone/rclone:1.68
              command:
                - rclone
                - sync
                - gravenhollow-s3:avatar-models
                - r2:avatar-models
                - --checksum
                - --transfers=4
                - -v
              volumeMounts:
                - name: rclone-config
                  mountPath: /config/rclone
                  readOnly: true
          volumes:
            - name: rclone-config
              secret:
                secretName: rclone-r2-config
          restartPolicy: OnFailure

rclone Config (ExternalSecret from Vault)

[gravenhollow-s3]
type = s3
provider = Other
endpoint = https://gravenhollow.lab.daviestechlabs.io:30292
access_key_id = <from-vault>
secret_access_key = <from-vault>

[r2]
type = s3
provider = Cloudflare
endpoint = https://<account-id>.r2.cloudflarestorage.com
access_key_id = <from-vault>
secret_access_key = <from-vault>
region = auto

Client-Side Routing

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

// avatar.js — model URL resolution
function resolveModelURL(path) {
    // LAN users: serve from the Go server (NFS-backed, same origin)
    // Remote users: serve from Cloudflare R2 CDN
    const isLAN = location.hostname.endsWith('.lab.daviestechlabs.io');
    if (isLAN) return path; // e.g. /assets/models/Companion-A.vrm
    return `https://assets.daviestechlabs.io${path.replace('/assets', '')}`;  
    // → https://assets.daviestechlabs.io/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)	Mirror of NFS dir for R2 sync source	S3 API	CronJob rclone
Cloudflare R2 (avatar-models)	CDN-served copy for remote users	R2 public bucket	Global, zero egress fees

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.
• R2 credentials: Cloudflare R2 API token stored in Vault (kv/data/cloudflare-r2), accessed via ExternalSecret by the sync CronJob. Write-only scope — the CronJob uploads but cannot delete the bucket.
• Public R2 bucket: Avatar models are public assets (served to any authenticated companions-chat user). No sensitive data in VRM files. R2 bucket is read-only public via custom domain; write access requires the API token.
• 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 + Cloudflare R2

• 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, and Cloudflare are 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 Cloudflare R2 has zero egress fees and global CDN caching for remote users
• Good, because immutable versioned filenames enable aggressive caching and trivial rollback
• 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 two-hop promotion path adds operational complexity

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 (companions-frontend architecture — Three.js + VRM avatars)
• Related to ADR-0026 (storage strategy — gravenhollow NFS-fast, Quobyte S3, rclone)
• Related to ADR-0044 (DNS and external access — Cloudflare Tunnel, split-horizon)
• Related to ADR-0049 (Kasm Workspaces)
• BlenderMCP GitHub
• VRM Add-on for Blender
• VRM Specification
• @pixiv/three-vrm (runtime loader used in companions-frontend)
• Poly Haven (free 3D assets, HDRIs, textures)
• Hyper3D Rodin (AI 3D model generation)
• Cloudflare R2 Documentation
• Cloudflare R2 Custom Domains