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+ # Model Repository Documentation
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+
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+ ## Repository Structure Overview
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+
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+ The repository is organized into eight main directories, each serving a specific purpose in the pipeline:
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+
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+ ### Meta Data (1_meta_data)
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+ Contains AMASS dataset metadata specifically focused on copycat and occlusion information, essential for motion capture applications.
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+
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+ ### MediaPipe Models (2_mediapipe_ckpts)
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+ Houses MediaPipe's specialized models for facial landmarks and hand tracking, providing fundamental capabilities for human pose estimation.
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+
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+ ### 4DHumans Framework (3_4DHumans)
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+ 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.
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+
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+ ### SMPLhub (4_SMPLhub)
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+ Serves as a comprehensive collection of human body models, including:
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+ - MANO (hand model) parameters for both left and right hands
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+ - SMPL models in various formats (NPZ and PKL) for male, female, and neutral body types
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+ - SMPLH (SMPL with detailed hand articulation)
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+ - SMPLX (extended SMPL model with face and hand expressions)
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+
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+ ### Additional Components
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+ - S3FD (5_S3FD): Contains face detection model weights
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+ - SyncNet (6_SyncNet): Includes audio-visual synchronization model
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+ - SGHM (7_SGHM): Houses ResNet50-based model weights
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+ - KonIQ (8_koniq): Contains pre-trained weights for image quality assessment
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+
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+ ```
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+ β”œβ”€β”€ 1_meta_data
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+ β”‚ └── amass_copycat_occlusion_v3.pkl
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+ β”œβ”€β”€ 2_mediapipe_ckpts
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+ β”‚ β”œβ”€β”€ face_landmarker.task
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+ β”‚ └── hand_landmarker.task
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+ β”œβ”€β”€ 3_4DHumans
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+ β”‚ β”œβ”€β”€ data
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+ β”‚ β”‚ β”œβ”€β”€ smpl
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+ β”‚ β”‚ β”‚ └── SMPL_NEUTRAL.pkl
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+ β”‚ β”‚ β”œβ”€β”€ smpl_mean_params.npz
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+ β”‚ β”‚ └── SMPL_to_J19.pkl
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+ β”‚ └── logs
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+ β”‚ └── train
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+ β”‚ └── multiruns
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+ β”‚ └── hmr2
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+ β”‚ └── 0
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+ β”‚ β”œβ”€β”€ checkpoints
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+ β”‚ β”‚ └── epoch=35-step=1000000.ckpt
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+ β”‚ β”œβ”€β”€ dataset_config.yaml
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+ β”‚ └── model_config.yaml
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+ β”œβ”€β”€ 4_SMPLhub
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+ β”‚ β”œβ”€β”€ 0_misc_files
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+ β”‚ β”‚ └── J_regressor_coco.npy
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+ β”‚ β”œβ”€β”€ MANO
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+ β”‚ β”‚ └── pkl
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+ β”‚ β”‚ β”œβ”€β”€ MANO_LEFT.pkl
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+ β”‚ β”‚ β”œβ”€β”€ mano_mean_params.npz
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+ β”‚ β”‚ └── MANO_RIGHT.pkl
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+ β”‚ β”œβ”€β”€ SMPL
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+ β”‚ β”‚ β”œβ”€β”€ basicmodel_X_lbs_10_207_0_v1.1.0_pkl
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+ β”‚ β”‚ β”‚ β”œβ”€β”€ basicmodel_f_lbs_10_207_0_v1.1.0.pkl
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+ β”‚ β”‚ β”‚ β”œβ”€β”€ basicmodel_m_lbs_10_207_0_v1.1.0.pkl
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+ β”‚ β”‚ β”‚ └── basicmodel_neutral_lbs_10_207_0_v1.1.0.pkl
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+ β”‚ β”‚ β”œβ”€β”€ X_model_npz
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+ β”‚ β”‚ β”‚ β”œβ”€β”€ SMPL_F_model.npz
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+ β”‚ β”‚ β”‚ β”œβ”€β”€ SMPL_M_model.npz
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+ β”‚ β”‚ β”‚ └── SMPL_N_model.npz
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+ β”‚ β”‚ └── X_pkl
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+ β”‚ β”‚ β”œβ”€β”€ SMPL_FEMALE.pkl
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+ β”‚ β”‚ β”œβ”€β”€ SMPL_MALE.pkl
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+ β”‚ β”‚ └── SMPL_NEUTRAL.pkl
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+ β”‚ β”œβ”€β”€ SMPLH
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+ β”‚ β”‚ β”œβ”€β”€ X_npz
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+ β”‚ β”‚ β”‚ β”œβ”€β”€ SMPLH_FEMALE.npz
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+ β”‚ β”‚ β”‚ β”œβ”€β”€ SMPLH_MALE.npz
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+ β”‚ β”‚ β”‚ └── SMPLH_NEUTRAL.npz
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+ β”‚ β”‚ └── X_pkl
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+ β”‚ β”‚ β”œβ”€β”€ SMPLH_female.pkl
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+ β”‚ β”‚ β”œβ”€β”€ SMPLH_male.pkl
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+ β”‚ β”‚ └── SMPLH_NEUTRAL.pkl
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+ β”‚ └── SMPLX
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+ β”‚ β”œβ”€β”€ mod
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+ β”‚ β”‚ └── SMPLX_MALE_shape2019_exp2020.npz
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+ β”‚ └── X_npz
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+ β”‚ β”œβ”€β”€ SMPLX_FEMALE.npz
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+ β”‚ β”œβ”€β”€ SMPLX_MALE.npz
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+ β”‚ └── SMPLX_NEUTRAL.npz
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+ β”œβ”€β”€ 5_S3FD
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+ β”‚ └── sfd_face.pth
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+ β”œβ”€β”€ 6_SyncNet
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+ β”‚ └── syncnet_v2.model
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+ β”œβ”€β”€ 7_SGHM
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+ β”‚ └── SGHM-ResNet50.pth
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+ └── 8_koniq
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+ └── koniq_pretrained.pkl
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+ ```