# HandFlow V1 — inference + render deps (no training deps)
# One-line install: pip install -r requirements.txt
# Or use setup_env.sh to create a conda environment

--extra-index-url https://download.pytorch.org/whl/cu128

# -- PyTorch (CUDA 12.8; matches the ViPE environment, see setup_vipe_env.sh) --
torch==2.7.0+cu128
torchvision==0.22.0+cu128

# -- Inference core --
numpy
scipy
omegaconf
tqdm

# -- Hand detection + MANO FK --
ultralytics          # YOLO detection (detector.pt weights from WiLoR, see README)
manopth              # MANO forward kinematics

# -- Rendering (demo.py -> mp4, pytorch3d Phong shading) --
# pytorch3d must match the torch/CUDA above; no prebuilt wheel for torch 2.7+cu128, so build from source:
#   pip install "git+https://github.com/facebookresearch/pytorch3d.git@v0.7.8"
pytorch3d
opencv-python
imageio[ffmpeg]
matplotlib

# The following two are installed separately as submodules:
#   HaMeR backbone:  pip install -e third_party/hamer
#   ViPE SLAM (optional): see setup_vipe_env.sh (standalone environment)