3ddfa
Guide 3DDFA Python inference, geometry rendering, training/evaluation, and optional C++ ONNX workflows for 3D dense face alignment.
Skill by VectorSpaceLab
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Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/vectorspacelab-3ddfa
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
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About
Use cases: ml
Reviews
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Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
- Runs as
- Python package
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/3ddfa
What's inside
- Skill name
3ddfa- Description
- Guide 3DDFA Python inference, geometry rendering, training/evaluation, and optional C++ ONNX workflows for 3D dense face alignment.
- License
- MIT
- Metadata
- disco-role: operating
- Instructions
- 61 lines in SKILL.md · view on GitHub
Before installing
- Ships executable scripts
Files (32)
- SKILL.md
- install-and-compatibility.md · references/
- repo-provenance.md · references/
- repo-routing-metadata.json · references/
- troubleshooting.md · references/
- check_3ddfa_environment.py · scripts/
- SKILL.md · sub-skills/cpp-onnx-port/
- cpp-build-and-runtime.md · sub-skills/cpp-onnx-port/references/
- onnx-export.md · sub-skills/cpp-onnx-port/references/
- troubleshooting.md · sub-skills/cpp-onnx-port/references/
- export_mobilenet_to_onnx.py · sub-skills/cpp-onnx-port/scripts/
- SKILL.md · sub-skills/geometry-rendering/
- api-reference.md · sub-skills/geometry-rendering/references/
- data-artifacts.md · sub-skills/geometry-rendering/references/
- output-formats.md · sub-skills/geometry-rendering/references/
- rendering-and-cython.md · sub-skills/geometry-rendering/references/
- troubleshooting.md · sub-skills/geometry-rendering/references/
- images_to_video.py · sub-skills/geometry-rendering/scripts/
- smoke_geometry.py · sub-skills/geometry-rendering/scripts/
- SKILL.md · sub-skills/python-inference/
- cli-reference.md · sub-skills/python-inference/references/
- inference-workflows.md · sub-skills/python-inference/references/
- model-checkpoint-notes.md · sub-skills/python-inference/references/
- troubleshooting.md · sub-skills/python-inference/references/
- inspect_3ddfa_inference.py · sub-skills/python-inference/scripts/
- smoke_mobilenet_forward.py · sub-skills/python-inference/scripts/
- SKILL.md · sub-skills/training-evaluation/
- data-layout.md · sub-skills/training-evaluation/references/
- evaluation-benchmarks.md · sub-skills/training-evaluation/references/
- training-and-losses.md · sub-skills/training-evaluation/references/
- troubleshooting.md · sub-skills/training-evaluation/references/
- validate_training_args.py · sub-skills/training-evaluation/scripts/
SKILL.md (first lines)
# 3DDFA Repo Skill Use this repo skill when a task involves 3DDFA / 3D Dense Face Alignment: face alignment in full pose range, 68-point landmark prediction, dense 3D face vertices, pose boxes, PLY/OBJ export, depth/PNCC/PAF outputs, MobileNet-V1 checkpoints, 3DDFA training/evaluation, or the optional C++ OpenCV DNN port. This skill is an operating guide for a 3DDFA checkout or adapted codebase. It is self-contained: use the references and bundled scripts here for routing, command construction, diagnostics, and troubleshooting instead of reopening the original repository documentation. ## First Checks 1. Read [references/repo-provenance.md](references/repo-provenance.md) before deciding whether this skill matches a checkout. 2. Read [references/install-and-compatibility.md](references/install-and-compatibility.md) before installing dependencies or choosing CPU/CUDA/dlib/Cython paths. 3. Run [scripts/check_3ddfa_environment.py](scripts/check_3ddfa_environment.py) against the target checkout for a safe import/resource diagnostic. 4. If the task names a concrete workflow, route to the matching sub-skill below. Minimal diagnostic from this skill root: ```bash python scripts/check_3ddfa_environment.py --repo-root /path/to/3DDFA ``` The diagnostic checks resources and imports; it does not run native inference, training, downloads, CMake builds, or benchmarks. ## Route Map | User task or signal | Read | |---|---| | Run still-image inference, no-dlib bbox inference, inspect `main.py` flags, diagnose dlib/Cython startup, verify MobileNet forward shape, understand output filenames | [sub-skills/python-inference/SKILL.md](sub-skills/python-inference/SKILL.md) | | Decode 62-D parameters, ROI boxes, sparse/dense vertices, PLY/OBJ/`.mat`, pose matrices, depth/PNCC/PAF, Cython renderer, BFM/3DMM data artifacts, video-frame rendering | [sub-skills/geometry-rendering/SKILL.md](sub-skills/geometry-rendering/SKILL.md) | | Adapt training commands, choose WPDC/VDC/PDC, validate filelists/param files/data roots, resume checkpoints, interpret AFLW/AFLW2000 metrics | [sub-skills/training-evaluation/SKILL.md](sub-skills/training-evaluation/SKILL.md) | | Export MobileNet checkpoint to ONNX, place C++ weights, build/run OpenCV DNN demo, debug CMake/OpenCV/Yolo/ONNX issues | [sub-skills/cpp-onnx-port/SKILL.md](sub-skills/cpp-onnx-port/SKILL.md) | | Cross-cutting install/import/runtime failure | [references/troubleshooting.md](references/troubleshooting.md) | ## Operating Boundaries - Prefer CPU-safe diagnostics first. CUDA training/evaluation and GPU inference are optional capability paths and must be verified separately. - The unmodified Python image CLI imports `dlib` and render utilities before argument parsing. Even bbox-only workflows can fail at startup if Python `dlib` or the Cython render extension is missing. - Depth and PNCC require the compiled Cython mesh core; PLY/OBJ/landmarks can be planned separately, but the native CLI import path may still require the extension unless wrapped or patched. - Full training, benchmark extraction, and the C++ demo depend on external datasets, optional weights, system packages, or GPUs. Treat these as explicit prerequisites, not default verification steps. - Do not use this skill for 3DDFA_V2 unless the user explicitly asks to port concepts; this skill is based on the legacy 3DDFA repository snapshot in the provenance reference. ## Bundled Scripts - [scripts/check_3ddfa_environment.py](scripts/check_3ddfa_environment.py) — shared checkout/resource/import diagnostic. - [sub-skills/python-inference/scripts/inspect_3ddfa_inference.py](sub-skills/python-inference/scripts/inspect_3ddfa_inference.py) — image/video inference-specific diagnostic and command planner. - [sub-skills/python-inference/scripts/smoke_mobilenet_forward.py](sub-skills/python-inference/scripts/smoke_mobilenet_forward.py) — safe MobileNet architecture forward smoke. - [sub-skills/geometry-rendering/scripts/smoke_geometry.py](sub-skills/geometry-rendering/scripts/smoke_geometry.py) — safe 3DMM reconstruction shape smoke. - [sub-skills/training-evaluation/scripts/validate_training_args.py](sub-skills/training-evaluation/scripts/validate_training_args.py) — training command/data-layout checker that does not launch training. - [sub-skills/cpp-onnx-port/scripts/export_mobilenet_to_onnx.py](sub-skills/cpp-onnx-port/scripts/export_mobilenet_to_onnx.py) — explicit ONNX export helper for the optional C++ port. ## Verification Expectations Safe verification usually includes: - package/import/resource diagnostics; - MobileNet CPU forward shape `(1, 62)`; - geometry reconstruction shapes `(3, 68)` and dense `(3, 53215)`; - script `--help` checks for bundled helpers; - explicit skip notes for dlib predictor, Cython build, CUDA, external datasets, and OpenCV C++ demo when unavailable. Do not claim native end-to-end inference, GPU training/evaluation, full benchmarks, or C++ runtime success unless those exact paths were run in the target environment.
From VectorSpaceLab/AREX-Skill, MIT.
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Signals
- Stars
- 341
- Forks
- 26
- Open issues
- 0
- Last commit (this folder)
- Aug 26, 2026 (2 months ago)
- Last release
- Sep 2, 2026 (2 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/VectorSpaceLab/AREX-Skill
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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3ddfa skill. Agent Mart. https://agentmart.dev/skills/vectorspacelab-3ddfa (last updated 2026-10-24)