liyang
ae28d55f81
Source: RGB-D -> Dyn-HaMR -> L20 retargeting -> FoundationPose -> reference repair -> SPIDER, documented in docs/PIPELINE_LATEST.md and docs/SETUP_AND_WEIGHTS.md. Adds FoundationPose and nvdiffrast upstream snapshots, requirements/pipeline_venv.txt and the FoundationPose weight manifest/downloader. Assets (Git LFS): weights/ (WiLoR detector, HandFlow denoiser, UniDepth-L), FoundationPose checkpoints, HaMeR checkpoint, Dyn-HaMR HMP model and BMC constraints, L20 URDF/meshes, the 20260915_171525 D405 recording and the two box CADs. MANO models are not redistributed (third_party/hamer/_DATA/data/mano/README.txt). Environments, caches and run outputs excluded. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
44 lines
938 B
Plaintext
44 lines
938 B
Plaintext
# Install PyTorch first (CUDA build must match your driver; cu124 works on most recent NVIDIA GPUs):
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# pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124
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#
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# Then GPU extensions (need nvcc; set CUDA_HOME to your toolkit, e.g. /usr/local/cuda):
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# pip install --no-build-isolation git+https://github.com/facebookresearch/pytorch3d.git
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# pip install --no-build-isolation git+https://github.com/NVlabs/nvdiffrast.git
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#
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# Then this file:
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# pip install -r requirements.txt
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# Core numerics & IO
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numpy>=2
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scipy
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scikit-learn
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h5py
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joblib
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# Config / serialization
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PyYAML>=6
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ruamel.yaml>=0.18
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# Vision & 3D
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opencv-python
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imageio
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open3d
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trimesh
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transformations
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matplotlib
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pandas
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Pillow
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pyrender
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pyOpenGL>=3.1
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pyOpenGL_accelerate>=3.1
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# Inference stack (torch installed separately)
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kornia>=0.7
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omegaconf>=2.3
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psutil
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tqdm
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warp-lang
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# Optional: faster / utility (import guarded in code)
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scikit-image
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