FaceFit — MPFB2 head ↔ reference-photo fitting via MCP

状态 / Status: prototype (v0.1)。在无 GPU 的 Linux 上用 Blender 4.2.3 LTS（Workbench 软件渲染）+ MPFB2 2.0.18 + MediaPipe（CPU）测试通过。

中文简介

FaceFit 是一个开源的 Blender 插件加 MCP 服务器。它把 MPFB2（MakeHuman for Blender）人头的面部滑杆拟合到一张正脸参考图上（目标是国漫 CG 风格的半写实脸），Codex 之类的外部 agent 可以通过 MCP 驱动整个拟合流程。

流程：MediaPipe Face Landmarker 检测 478 个点 → 取 57 个稳定点（眼角、眼睑、眉、鼻、嘴、下颌轮廓）→ 对应到 MPFB 基础网格顶点（data/mp_to_mpfb.json，由 scripts/calibrate_correspondences.py 自动标定）→ 正交正视投影 + 相似变换对齐 → 用 scipy 有界线性最小二乘（lsq_linear，带岭正则）交替求解约 48 个面部滑杆。

Related MCP server: mcp-vroid

English

Blender add-on (blender_addon/facefit_bridge, a JSON socket bridge in the style of blender-mcp) plus a Python MCP server (facefit/mcp_server.py, official mcp SDK, stdio). Landmark detection and solving run in the MCP server process. Blender handles the MPFB mesh, shape keys, rendering and export.

Install

# 1. Blender >= 4.2 (tested 4.2.3 LTS, headless OK, no GPU needed for Workbench)
curl -LO https://download.blender.org/release/Blender4.2/blender-4.2.3-linux-x64.tar.xz && tar xf blender-4.2.3-linux-x64.tar.xz
# Debian/Ubuntu headless libs: sudo apt install libegl1 libgl1 libxi6 libxxf86vm1 libxfixes3 libxrender1 libxkbcommon0 libsm6
# 2. MPFB2 as a Blender extension (bundled base mesh + targets are enough for fitting)
git clone --depth 1 https://github.com/makehumancommunity/mpfb2
(cd mpfb2/src && zip -r ../../mpfb.zip mpfb)
blender -b --command extension install-file -r user_default -e mpfb.zip
# 3. Python env for MCP server
python3 -m venv .venv && .venv/bin/pip install -r requirements.txt
scripts/download_models.sh   # MediaPipe landmarker, BiSeNet face-parsing ONNX, MakeHuman eyes (CC0) into MPFB user data

Optional MPFB "system assets" pack (eyes/eyebrows/skins): https://static.makehumancommunity.org/assets/assetpacks/makehuman_system_assets.html. Fitting does not need it. On our test box the 280 MB download ran at about 12 KB/s, so we skipped it and render untextured clay.

Run

BLENDER=/path/to/blender scripts/run_blender_headless.sh &      # headless bridge on 127.0.0.1:9876
.venv/bin/python scripts/e2e_test.py testdata/your_face.png     # -> out/before.png after.png overlay.png report.json fitted.{glb,obj,stl}
.venv/bin/python scripts/e2e_v2.py testdata/your_face.png       # phase 2: depth + texture -> out/v2_textured_front.png, v2_textured_34.png, out/textures/*
.venv/bin/python scripts/mcp_smoke_test.py                      # drives the MCP server over stdio

GUI alternative: install blender_addon/facefit_bridge as an add-on/extension, then open the N-panel ▸ FaceFit ▸ Start server.

Codex MCP config (~/.codex/config.toml)

[mcp_servers.facefit]
command = "/path/to/facefit/.venv/bin/python"
args = ["-m", "facefit.mcp_server"]
cwd = "/path/to/facefit"
env = { FACEFIT_PORT = "9876", FACEFIT_OUT = "/path/to/facefit/out" }

(Equivalent: codex mcp add facefit -- /path/to/facefit/.venv/bin/python -m facefit.mcp_server, which must run with the repo as cwd.)

MCP tools

load_reference_image, detect_landmarks, new_human, auto_fit(reg, params, style, locked), list_parameters, get_parameters, set_parameters, render_view(overlay, markers), get_landmark_error, undo, save_state, load_state, export_model(glb|obj|stl), project_texture, render_textured(yaw_deg), get_depth_error.

Stylisation: auto_fit(style={"eye-scale-decr-incr": 0.2, "chin-triangle": 0.4}) adds offsets after the fit (bigger eyes, pointier chin).

Result on our private test image (front-facing guoman CG face, not distributed)

Phase 2 (v0.2)

• Texture projection (project_texture): per-triangle affine warps take the photo into MPFB base-mesh UVs (4096²). A z-buffer handles occlusion, and each triangle is weighted by its normal. BiSeNet face parsing (ONNX, CPU) keeps hair and background out of the projection and writes UV-space masks and RGBA layers for skin, brows, lips, eyes and mouth (out/textures/). Unseen texels get a skin colour matched to the projection border, with a feathered seam and Telea inpainting. The eyes are MakeHuman high-poly eyes (CC0). Their iris is recoloured from the parsed reference iris, because the photo's iris is too small to project.

• Depth constraint (auto_fit(depth_weight=0.3)): MediaPipe z is used for the brows, nose and mouth points. The depth scale and offset are free, and the jaw silhouette and eyelid points are excluded. This adds 14 depth sliders (nose projection and hump, chin prognathism, mouth and brow depth, and others).

Phase 3 (v0.3): likeness

scripts/e2e_v3.py runs the full chain. The same chain over MCP is auto_fit(fit_macros=True, vertex_refine=True) → project_face_photo() → render_textured().

1. Macro search: tries female, age 25 presets with Asian ratio and proportions variants, and keeps the one with the lowest landmark error.

2. Slider fit: 2D landmarks plus MediaPipe depth, as in v0.2.

3. Vertex refine: shape key facefit_refine, a thin-plate RBF in the front plane. Its controls are the 57 landmark vertices plus about 22 lower-face silhouette vertices matched to the BiSeNet face-mask outline. Neck, back-of-head and skull-top anchors hold the rest in place. The result is mirror-symmetrised, and two passes are run.

4. Style keys style_big_eyes, style_small_nose, style_full_lips, style_v_chin are set with set_style.

5. Face texture: the cleaned photo is camera-projected through a dedicated per-vertex UV (facefit_proj), so it keeps the photo's native resolution. The cleaning step inpaints parsed hair, earrings, thin strands and the outline band, then applies mild LAB low-frequency de-lighting. The projection is feathered into the body skin colour by a visibility × face-mask attribute.

6. Eyes and hair: MakeHuman eyes with the iris recoloured to match the reference, and a simple hair cap with extruded side curtains.

The textured-render check is biased, because the texture is the photo itself.

Phase 4 (v0.4): full-head / bald references

scripts/e2e_bald.py your_bald.jpg

• Parsing crop: BiSeNet runs on a larger crop (2.2×, shifted up) so the whole skull is segmented.

• Full-head refine (vertex_refine(full_head=True), auto when no hair is parsed):

  • Polar silhouette matching every 3° against the parsed head mask (skull, forehead, ears, jaw), plus neck-width rows.

  • Two stages: a smooth 2D silhouette field applied to the whole head, then a local 3D landmark residual field.

  • Cranium depth is stretched with its height so the dome stays round.

• Eyes:

  • The photo's irises are pasted into the eye texture; eyelid-occluded parts are filled by polar symmetry.

  • Cornea shells are removed, and eyeball specular is kept low under the soft key light.

  • calibrate_eyes scales and moves the eyeballs from MediaPipe iris landmarks measured on a render.

  • Eyelashes come from a photo lash mask composited onto the eyeballs through camera projection.

• Soft frontal lighting on a grey background (render_textured(light="soft"), the default).

• Metrics: silhouette_iou.

• Ears: ear-flap is set to 1.0 by hand. Ears in a frontal photo appear face-on, but they are not visible in the landmarks.

Limitations / TODO

• Depth only comes from MediaPipe's monocular z, which is weak on stylised CG faces, so the gain is small (16.0→14.4 %IOD). A profile reference or a 3D face prior would do better.

• Correspondences come from MediaPipe run on a clay render of the default MPFB head (about 4 px reprojection error at 1024 px). Jaw-contour points are silhouette-dependent and only approximate.

• MPFB slider ranges cannot reach extreme anime proportions (very large eyes). The residual error is mostly from the eyes and jaw.

• The texture carries the photo's baked lighting. The forehead and temples under the hair are filled with a flat colour, and faint hair/earring streaks can remain near the jaw. In MPFB's base UV the face covers only about 25% of the texture width. Not done yet: hair, eyebrow geometry, delighting, normal maps.

• Licenses: code GPL-3.0-or-later (it drives MPFB, which is GPL). MPFB assets are CC0. The MediaPipe model is Apache-2.0.