---

name: "3ddfa-v2" description: "Routes 3DDFA_V2 face-alignment setup, still-image demos, video tracking, and ONNX benchmarking workflows." metadata: disco-role: operating disable-model-invocation: true license: MIT

3DDFA_V2

Use this skill for the 3DDFA_V2 face-alignment repo. The public workflow is a small pipeline: build the native pieces, then run still-image, video/tracking, or ONNX/benchmark commands.

Start here

1. If anything fails to build or import, open references/troubleshooting.md.
2. If you need model files or config choices, open references/model-assets.md.
3. If you need class/function details, open references/api-reference.md.
4. Run the setup route first whenever render.so, cpu_nms, or Sim3DR_Cython is missing.

The repository is source-first, not a packaged wheel. Use the bundled helpers in this skill tree instead of calling the original repo scripts directly.

Routes

setup-and-assets

Use when the user asks to install or verify the runtime, build compiled pieces, check checkpoint/config assets, or fix import/build failures.

Read sub-skills/setup-and-assets/SKILL.md, references/model-assets.md, and references/troubleshooting.md.

Use the bundled helpers:

• scripts/build_native_extensions.py
• scripts/check_assets.py
• scripts/check_core_imports.py

still-image-demo

Use for single-image inference, 2D landmark overlays, 3D renderings, depth, PNCC, UV texture, pose boxes, PLY, or OBJ exports.

Read sub-skills/still-image-demo/SKILL.md and sub-skills/still-image-demo/references/workflows.md.

Use sub-skills/still-image-demo/scripts/run-still-image.py for a headless-friendly wrapper.

video-and-tracking

Use for MP4/AVI processing, tracking, smoothing, or frame-window control.

Read sub-skills/video-and-tracking/SKILL.md and sub-skills/video-and-tracking/references/workflows.md.

Use sub-skills/video-and-tracking/scripts/run-video.py and sub-skills/video-and-tracking/scripts/run-video-smooth.py.

onnx-and-benchmarking

Use for ONNX acceleration, CPU latency, thread tuning, or microbenchmarks.

Read sub-skills/onnx-and-benchmarking/SKILL.md and sub-skills/onnx-and-benchmarking/references/workflows.md.

Use sub-skills/onnx-and-benchmarking/scripts/run-latency.py and sub-skills/onnx-and-benchmarking/scripts/run-speed-cpu.py.

Common runtime facts

• Default config: configs/mb1_120x120.yml.
• Alternate configs: configs/mb05_120x120.yml and configs/resnet_120x120.yml.
• Provide an input image or video path for demos; local smoke fixtures may be used when the checkout includes them.
• Generated outputs live under examples/results/.
• demo.py supports 2d_sparse, 2d_dense, 3d, depth, pncc, uv_tex, pose, ply, and obj.
• demo_video.py and demo_video_smooth.py support 2d_sparse and 3d; webcam mode is manual-only and is documented, but not bundled as a runnable helper.
• --onnx switches the demo pipeline to the CPU-friendly ONNX path.
• uv_tex needs SciPy and the BFM UV/config assets.
• The repo still references deprecated NumPy aliases such as np.long, so the bundled runtime helpers restore a compatibility layer before importing the pipeline.

Headless use

The bundled helpers default to headless plotting behavior so they work in non-GUI environments. If you need interactive windows, override that behavior explicitly.

What not to route here

• Experimental Gradio notebook/demo code.
• Generic face detection tasks that do not involve the 3DDFA_V2 alignment pipeline.
• Training or dataset creation tasks; this repo is inference-oriented.