Markerless Kinematic Analysis (MKA)

Setup Instructions

1. Environment Setup

Create and configure the conda environment:

conda create -n mka python=3.8 -y
conda activate mka
pip install torch==1.13.1+cu117 torchvision==0.14.1+cu117 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu117
conda install https://anaconda.org/pytorch3d/pytorch3d/0.7.5/download/linux-64/pytorch3d-0.7.5-py38_cu117_pyt1131.tar.bz2
pip install -r requirements.txt

cd dependencies
git clone https://github.com/ViTAE-Transformer/ViTPose.git
cd ..
pip install -v -e dependencies/ViTPose

cd dependencies/cpp_module
sh install.sh 
cd ../..

If build cpp_module fail, you can try conda-based gcc:

conda install -c conda-forge gcc=9 gxx=9
conda install -c conda-forge libxcrypt

You also need to setup SAM2 environment following facebookresearch/sam2

2. Human Models

Human body model files are required for body, hand, and face parameterization.

human_models/
│── human_models.py
└── human_model_files/
    ├── J_regressor_extra.npy
    ├── J_regressor_h36m.npy
    ├── mano_mean_params.npz
    ├── smpl_mean_params.npz
    ├── smpl/
    │   ├── SMPL_FEMALE.pkl
    │   ├── SMPL_MALE.pkl
    │   └── SMPL_NEUTRAL.pkl
    ├── smplx/
    │   ├── SMPLX_FEMALE.pkl
    │   ├── SMPLX_MALE.pkl
    │   ├── SMPLX_NEUTRAL.pkl
    │   ├── SMPLX_to_J14.pkl
    │   ├── SMPL-X__FLAME_vertex_ids.npy
    │   └── MANO_SMPLX_vertex_ids.pkl
    └── mano/
        └── MANO_RIGHT.pkl

Here we provide some download links for the files:

3. Pretrained Weights

Pretrained models are required for pose detection and human mesh recovery.

pretrained_models/
├── yolov8x.pt
├── sam2.1_hiera_large.pt
├── hamer_ckpts/
│   ├── dataset_config.yaml
│   ├── model_config.yaml
│   └── checkpoints/
│       └── hamer.ckpt
├── smplest_x_h/
│   ├── config_base.py
│   └── smplest_x_h.pth.tar
└── vitpose_ckpts/
    └── vitpose+_huge/
        └── wholebody.pth

Some instructions:

Running the Pipeline

To execute the full processing pipeline:

bash run_pipeline.sh

Explore More Motrix Projects

Motion Capture

[SMPL-X] [TPAMI'25] SMPLest-X: An extended version of SMPLer-X with stronger foundation models.
[SMPL-X] [NeurIPS'23] SMPLer-X: Scaling up EHPS towards a family of generalist foundation models.
[SMPL-X] [ECCV'24] WHAC: World-grounded human pose and camera estimation from monocular videos.
[SMPL-X] [CVPR'24] AiOS: An all-in-one-stage pipeline combining detection and 3D human reconstruction.
[SMPL-X] [NeurIPS'23] RoboSMPLX: A framework to enhance the robustness of whole-body pose and shape estimation.
[SMPL-X] [ICML'25] ADHMR: A framework to align diffusion-based human mesh recovery methods via direct preference optimization.
[SMPL-X] MKA: Full-body 3D mesh reconstruction from single- or multi-view RGB videos.
[SMPL] [ICCV'23] Zolly: 3D human mesh reconstruction from perspective-distorted images.
[SMPL] [IJCV'26] PointHPS: 3D HPS from point clouds captured in real-world settings.
[SMPL] [NeurIPS'22] HMR-Benchmarks: A comprehensive benchmark of HPS datasets, backbones, and training strategies.

Motion Generation

[SMPL-X] [ICLR'26] ViMoGen: A comprehensive framework that transfers knowledge from ViGen to MoGen across data, modeling, and evaluation.
[SMPL-X] [ECCV'24] LMM: Large Motion Model for Unified Multi-Modal Motion Generation.
[SMPL-X] [NeurIPS'23] FineMoGen: Fine-Grained Spatio-Temporal Motion Generation and Editing.
[SMPL] InfiniteDance: A large-scale 3D dance dataset and an MLLM-based music-to-dance model designed for robust in-the-wild generalization.
[SMPL] [NeurIPS'23] InsActor: Generating physics-based human motions from language and waypoint conditions via diffusion policies.
[SMPL] [ICCV'23] ReMoDiffuse: Retrieval-Augmented Motion Diffusion Model.
[SMPL] [TPAMI'24] MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model.

Motion Dataset

[SMPL] [ECCV'22] HuMMan: Toolbox for HuMMan, a large-scale multi-modal 4D human dataset.
[SMPLX] [T-PAMI'24] GTA-Human: Toolbox for GTA-Human, a large-scale 3D human dataset generated with the GTA-V game engine.

MKA

Install / Use

README