Densemarks

--- license: cc-by-nc-4.0 tags: - computer-vision - human-head - humans - landmarks - pytorch pipeline_tag: image-feature-extraction library_name: pytorch --- # DenseMarks A PyTorch implementation for dense UVW coordinate prediction from human head images using DINOv3 backbone with DPT head architecture. ## Overview DenseMarks predicts per-pixel positions in canonical space (cube [0, 1]³) from human head images. **Input**: RGB images of size 512×512 pixels **Output**: UVW coordinates tensor (B, 3, 512, 512) with values in [0, 1] ## Prerequisites - Python 3.8+ - PyTorch 1.12+ - CUDA (optional, for GPU acceleration) ## Installation 1. **Clone the repository:** ```bash git clone https://github.com/diddone/densemarks.git cd densemarks ``` 2. **Install DINOv3 submodule:** ```bash git clone https://github.com/facebookresearch/dinov3 third_party_dinov3 ``` 3. **Modify DINOv3 for compatibility:** ```bash # For Linux (GNU sed): sed -i '/dinov3\.hub\.segmentors/s/^/#/; /dinov3\.hub\.classifiers/s/^/#/; /dinov3\.hub\.detectors/s/^/#/; /dinov3\.hub\.dinotxt/s/^/#/; /dinov3\.hub\.depthers/s/^/#/' third_party_dinov3/hubconf.py # For macOS (BSD sed): sed -i '' '/dinov3\.hub\.segmentors/s/^/#/; /dinov3\.hub\.classifiers/s/^/#/; /dinov3\.hub\.detectors/s/^/#/; /dinov3\.hub\.dinotxt/s/^/#/; /dinov3\.hub\.depthers/s/^/#/' third_party_dinov3/hubconf.py ``` 4. **Install dependencies:** ```bash pip install torch transformers numpy ``` 5. **Download model weights from Hugging Face:** ```python3 from dense_marks_model import DenseMarksModel, read_image from huggingface_hub import hf_hub_download model = DenseMarksModel(hf_hub_download("diddone/densemarks", "model.safetensors")) images = read_image("assets/00000.png") # rgb, 512x512 uvw = model(images) # Predict UVW coordinates ```

Source context: 0 downloads · 2 likes · Pipeline image-feature-extraction · Library pytorch · Repo diddone/densemarks

Source context: 0 downloads · 1 likes · Pipeline image-feature-extraction · Library pytorch · Repo diddone/densemarks