File size: 1,350 Bytes
aa7adfd df39e39 aa7adfd 887af40 aa7adfd 887af40 aa7adfd 887af40 aa7adfd 887af40 aa7adfd 887af40 aa7adfd 887af40 aa7adfd 887af40 aa7adfd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 |
---
title: SHeaP - Self-Supervised Head Geometry Predictor
emoji: 🐑
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: "5.50.0"
app_file: gradio_demo.py
pinned: false
license: mit
---
# SHeaP: Self-Supervised Head Geometry Predictor Learned via 2D Gaussians
Upload an image or video to predict head geometry and render a 3D FLAME mesh overlay!
**Liam Schoneveld, Zhe Chen, Davide Davoli, Jiapeng Tang, Saimon Terazawa, Ko Nishino, Matthias Nießner**
- [Project Page](https://nlml.github.io/sheap)
- [Paper](https://arxiv.org/abs/2504.12292)
- [GitHub Repository](https://github.com/nlml/sheap)
## About
SHeaP learns to predict head geometry (FLAME parameters) from a single image by predicting and rendering 2D Gaussians.
The output shows three views:
- **Left**: Original cropped face
- **Center**: Rendered FLAME mesh
- **Right**: Mesh overlaid on original
## Setup Instructions
Before deploying to Hugging Face Spaces, you need to:
1. Download the FLAME model from [FLAME 2020](https://flame.is.tue.mpg.de/)
2. Convert it using `python convert_flame.py`
3. Include the `FLAME2020/` directory with the required files:
- `generic_model.pt`
- `eyelids.pt`
- `flame_landmark_idxs_barys.pt`
4. Include the `models/` directory with:
- `model_expressive.pt`
- `model_paper.pt`
- `model_lightweight.pt` (if available)
|