"Run 3DDFA_V2 still-image alignment, rendering, pose, texture, and
Scanned 9/8/2026
Install to Claude Code
npx -y skills add VectorSpaceLab/AREX-Skill --skill still-image-demo --agent claude-codeInstalls into .claude/skills of the current project.
Are you the author of Still Image Demo?
Add the live security badge to your README — it updates automatically with every re-scan.
[](https://www.skillsdirectory.com/skills/vectorspacelab-still-image-demo)More formats (shields.io, HTML) on the badges page.
---
name: "still-image-demo"
description: "Run 3DDFA_V2 still-image alignment, rendering, pose, texture, and
mesh export workflows."
metadata:
disco-role: operating
disable-model-invocation: true
license: MIT
---
# Still-image demo
Use this sub-skill for `demo.py` workflows on a single image: sparse or dense
landmarks, 3D renderings, depth, PNCC, UV texture, pose boxes, and PLY/OBJ mesh
exports.
## When to read
Read this sub-skill when the task asks to:
- Run 3DDFA_V2 on a still image.
- Generate `2d_sparse`, `2d_dense`, `3d`, `depth`, `pncc`, `uv_tex`, `pose`,
`ply`, or `obj` outputs.
- Use `--onnx` for a single-image demo.
- Diagnose `No face detected`, missing result files, headless plotting, or
mode-specific failures.
## Before running
1. Use `../setup-and-assets/` if native extensions or checkpoints are not known
to be ready.
2. Check `../../references/model-assets.md` when switching configs or missing
weight files.
3. In headless environments, keep `--show_flag false`; the bundled wrapper also
sets a headless plotting backend.
## Main wrapper
The bundled wrapper preserves the original `demo.py` CLI. Put original demo
arguments after `--`:
```bash
python <skill-root>/sub-skills/still-image-demo/scripts/run-still-image.py \
--repo-root <checkout> -- \
-f <image-path> -o 3d --show_flag false --onnx
```
Use direct repo commands only when you already applied the same compatibility
and headless setup the wrapper provides.
## Output selection
Read `references/workflows.md` for the full output-mode table. The highest-use
choices are:
- `2d_sparse` for 68-point landmark overlays.
- `2d_dense` for dense landmark visualization.
- `3d` for rendered dense mesh overlay.
- `depth`, `pncc`, and `uv_tex` for specialized per-pixel visual products.
- `pose` for yaw/pitch/roll pose-box visualization.
- `ply` and `obj` for mesh serialization.
Outputs default to `examples/results/` and use the input basename plus the
selected option.
## Decision points
- Prefer `--onnx` for CPU latency and when the `.onnx` assets are already
present or can be auto-converted.
- Use `configs/mb05_120x120.yml` when the task values speed over the default
backbone.
- Use dense reconstruction for `3d`, `depth`, `pncc`, `uv_tex`, `ply`, and
`obj`; sparse mode is enough for `2d_sparse` and `pose`.
- If the user needs multi-frame tracking, route to `../video-and-tracking/`.
- If the user asks about latency numbers rather than a saved image, route to
`../onnx-and-benchmarking/`.
## Troubleshooting
Read `references/troubleshooting.md` for still-image-specific failures and
`../../references/troubleshooting.md` for shared build/import failures.

Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.
No comments yet. Be the first to comment!