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name: python-inference description: "Operate 3DDFA Python image and video inference for landmarks, dense vertices, meshes, pose boxes, depth, PNCC, and PAF outputs." disable-model-invocation: true metadata: disco-role: operating license: MIT

Python Inference

Use this sub-skill when the task is to plan, diagnose, or run the 3DDFA Python inference surface for still images or the bundled video demo behavior. It covers:

• 68-point landmark inference from a 120x120 face crop.
• Dense vertex prediction and exported .mat, .ply, and textured .obj artifacts.
• Per-image pose-box, depth, PNCC, and optional PAF outputs.
• Bounding-box-driven inference that avoids using the dlib detector and dlib landmark model, while still accounting for the repo's top-level dlib import.
• Safe MobileNet forward checks and dependency/file diagnostics before invoking native inference.

Do not use this sub-skill for training/evaluation, benchmark interpretation, C++/ONNX conversion, or low-level 3DMM/rendering math. Route those to the sibling owner for the relevant workflow.

Start Here

1. Read references/cli-reference.md for the distilled image/video command surface and defaults.
2. Read references/inference-workflows.md for no-dlib bbox inference, output ownership, GPU/CPU handling, and video caveats.
3. Read references/model-checkpoint-notes.md before changing architecture, checkpoint, or parameter dimensions.
4. Run the bundled diagnostics from this sub-skill directory when working with a 3DDFA checkout:
   • python scripts/inspect_3ddfa_inference.py --repo-root /path/to/3DDFA
   • python scripts/smoke_mobilenet_forward.py --repo-root /path/to/3DDFA
5. If diagnostics report a failure mode, use references/troubleshooting.md before retrying.

Operating Rules

• Prefer bbox-driven inference (--dlib_bbox=false --dlib_landmark=false) when the user can provide <image>.bbox; this avoids the dlib detector and dlib landmark model but does not remove the need for the Python dlib module in the unmodified native CLI.
• Treat --mode gpu as an explicit CUDA request only. If CUDA is unavailable, verify the MobileNet path with the bundled CPU smoke script and do not claim GPU inference was validated.
• The native image CLI hard-codes models/phase1_wpdc_vdc.pth.tar, architecture mobilenet_1, and num_classes=62; changing any of these requires compatibility checks from the model reference.
• Depth and PNCC paths depend on the compiled Cython mesh core. Even when depth/PNCC flags are disabled, the unmodified native CLI imports the render module at startup, so diagnose Cython availability before relying on the CLI.
• Keep source-code reading out of runtime workflows: use this sub-skill's references and bundled scripts as the operating context.