Model Repository Documentation
Repository Structure Overview
The repository is organized into eight main directories, each serving a specific purpose in the pipeline:
Meta Data (1_meta_data)
Contains AMASS dataset metadata specifically focused on copycat and occlusion information, essential for motion capture applications.
MediaPipe Models (2_mediapipe_ckpts)
Houses MediaPipe's specialized models for facial landmarks and hand tracking, providing fundamental capabilities for human pose estimation.
4DHumans Framework (3_4DHumans)
Incorporates the SMPL (Skinned Multi-Person Linear Model) framework along with training artifacts. The directory includes model parameters, joint regressors, and HMR2 (Human Mesh Recovery) training checkpoints with corresponding configuration files.
SMPLhub (4_SMPLhub)
Serves as a comprehensive collection of human body models, including:
- MANO (hand model) parameters for both left and right hands
- SMPL models in various formats (NPZ and PKL) for male, female, and neutral body types
- SMPLH (SMPL with detailed hand articulation)
- SMPLX (extended SMPL model with face and hand expressions)
Additional Components
- S3FD (5_S3FD): Contains face detection model weights
- SyncNet (6_SyncNet): Includes audio-visual synchronization model
- SGHM (7_SGHM): Houses ResNet50-based model weights
- KonIQ (8_koniq): Contains pre-trained weights for image quality assessment
├── 1_meta_data
│ └── amass_copycat_occlusion_v3.pkl
├── 2_mediapipe_ckpts
│ ├── face_landmarker.task
│ └── hand_landmarker.task
├── 3_4DHumans
│ ├── data
│ │ ├── smpl
│ │ │ └── SMPL_NEUTRAL.pkl
│ │ ├── smpl_mean_params.npz
│ │ └── SMPL_to_J19.pkl
│ └── logs
│ └── train
│ └── multiruns
│ └── hmr2
│ └── 0
│ ├── checkpoints
│ │ └── epoch=35-step=1000000.ckpt
│ ├── dataset_config.yaml
│ └── model_config.yaml
├── 4_SMPLhub
│ ├── 0_misc_files
│ │ └── J_regressor_coco.npy
│ ├── MANO
│ │ └── pkl
│ │ ├── MANO_LEFT.pkl
│ │ ├── mano_mean_params.npz
│ │ └── MANO_RIGHT.pkl
│ ├── SMPL
│ │ ├── basicmodel_X_lbs_10_207_0_v1.1.0_pkl
│ │ │ ├── basicmodel_f_lbs_10_207_0_v1.1.0.pkl
│ │ │ ├── basicmodel_m_lbs_10_207_0_v1.1.0.pkl
│ │ │ └── basicmodel_neutral_lbs_10_207_0_v1.1.0.pkl
│ │ ├── X_model_npz
│ │ │ ├── SMPL_F_model.npz
│ │ │ ├── SMPL_M_model.npz
│ │ │ └── SMPL_N_model.npz
│ │ └── X_pkl
│ │ ├── SMPL_FEMALE.pkl
│ │ ├── SMPL_MALE.pkl
│ │ └── SMPL_NEUTRAL.pkl
│ ├── SMPLH
│ │ ├── X_npz
│ │ │ ├── SMPLH_FEMALE.npz
│ │ │ ├── SMPLH_MALE.npz
│ │ │ └── SMPLH_NEUTRAL.npz
│ │ └── X_pkl
│ │ ├── SMPLH_female.pkl
│ │ ├── SMPLH_male.pkl
│ │ └── SMPLH_NEUTRAL.pkl
│ └── SMPLX
│ ├── mod
│ │ └── SMPLX_MALE_shape2019_exp2020.npz
│ └── X_npz
│ ├── SMPLX_FEMALE.npz
│ ├── SMPLX_MALE.npz
│ └── SMPLX_NEUTRAL.npz
├── 5_S3FD
│ └── sfd_face.pth
├── 6_SyncNet
│ └── syncnet_v2.model
├── 7_SGHM
│ └── SGHM-ResNet50.pth
└── 8_koniq
└── koniq_pretrained.pkl
Create New Model Repo
Update LFS files
git lfs track "*.gif"
git lfs track "*.jpg"
git lfs track "*.png"
# 4. 使用 git lfs migrate 命令转换现有文件
# 这会将已经提交的文件转换为 LFS 对象
git lfs migrate import --include="*.gif,*.jpg,*.png" --everything
# 5. 强制推送更新后的历史
git push --force origin main
Add new repo
git add .
git commit -m "init"
git push