🌟 DPoser: Diffusion Model as Robust 3D Human Pose Prior 🌟

🔗 Project Page | 🎥 Video | 📄 Paper

Authors

Junzhe Lu, Jing Lin, Hongkun Dou, Ailing Zeng, Yue Deng, Yulun Zhang, Haoqian Wang

📊 An overview of DPoser’s versatility and performance across multiple pose-related tasks

📘 1. Introduction

Welcome to the official implementation of DPoser: Diffusion Model as Robust 3D Human Pose Prior. 🚀
In this repository, we're excited to introduce DPoser, a robust 3D human pose prior leveraging diffusion models. DPoser is designed to enhance various pose-centric applications like human mesh recovery, pose completion, and motion denoising. Let's dive in!

🔄 Switch to the 'v2' Branch for Enhanced Features!

We highly recommend switching to the 'v2' branch, which supports additional pose priors and features cleaner, more structured code. To switch, use the following command:

git checkout v2

🛠️ 2. Setup Your Environment

• Tested Configuration: Our code works great on PyTorch 1.12.1 with CUDA 11.3.

• Installation Recommendation:

  conda install pytorch==1.12.1 torchvision==0.13.1 cudatoolkit=11.3 -c pytorch

• Required Python Packages:

  pip install requirements.txt

• Body Models: We use the SMPLX body model in our experiments. Make sure to set the --bodymodel-path parameter correctly in scripts like ./run/demo.py and ./run/train.py based on your body model's download location.

🚀 3. Quick Demo

• Pre-trained Model: Grab the pre-trained DPoser model from here and place it in ./pretrained_models.

• Sample Data: Check out ./examples for some sample files, including 500 body poses from the AMASS dataset and a motion sequence fragment.

• Explore DPoser Tasks:

🎭 Pose Generation

Generate poses and save rendered images:

python -m run.demo --config configs/subvp/amass_scorefc_continuous.py  --task generation

For videos of the generation process:

python -m run.demo --config configs/subvp/amass_scorefc_continuous.py  --task generation_process

🧩 Pose Completion

Complete poses and view results:

python -m run.demo --config configs/subvp/amass_scorefc_continuous.py  --task completion --hypo 10 --part right_arm --view right_half

Explore other solvers like ScoreSDE for our DPoser prior:

python -m run.demo --config configs/subvp/amass_scorefc_continuous.py  --task completion2 --hypo 10 --part right_arm --view right_half

🌪️ Motion Denoising

Summarize visual results in a video:

python -m run.motion_denoising --config configs/subvp/amass_scorefc_continuous.py --file-path ./examples/Gestures_3_poses_batch005.npz --noise-std 0.04

🕺 Human Mesh Recovery

Use the detected 2D keypoints from openpose and save fitting results:

python -m run.demo_fit --img=./examples/image_00077.jpg --openpose=./examples/image_00077_keypoints.json

🧑‍🔬 4. Train DPoser Yourself

Dataset Preparation

To train DPoser, we use the AMASS dataset. You have two options for dataset preparation:

• Option 1: Process the Dataset Yourself
  Download the AMASS dataset and process it using the following script:

  python -m lib/data/script.py

  Ensure you follow this directory structure:

  ${ROOT}  
  |-- data  
  |  |-- AMASS  
  |     |-- amass_processed  
  |        |-- version1  
  |           |-- test  
  |              |-- betas.pt  
  |              |-- pose_body.pt  
  |              |-- root_orient.pt  
  |           |-- train  
  |           |-- valid  
  

• Option 2: Use Preprocessed Data
  Alternatively, download the processed data directly from Google Drive.

🏋️‍♂️ Start Training

After setting up your dataset, begin training DPoser:

python -m run.train --config configs/subvp/amass_scorefc_continuous.py --name reproduce

This command will start the training process. The checkpoints, TensorBoard logs, and validation visualization results will be stored under ./output/amass_amass.

🧪 5. Test DPoser

Pose Generation

Quantitatively evaluate 500 generated samples using this script:

python -m run.demo --config configs/subvp/amass_scorefc_continuous.py  --task generation --metrics

This will use the SMPL body model to evaluate APD and SI following Pose-NDF.

Pose Completion

For testing on the AMASS dataset (make sure you've completed the dataset preparation in Step 4):

python -m run.completion --config configs/subvp/amass_scorefc_continuous.py --gpus 1 --hypo 10 --sample 10 --part legs

Motion Denoising

To evaluate motion denoising on the AMASS dataset, use the following steps:

• Split the HumanEva part of the AMASS dataset into fragments using this script:

  python lib/dataset/HumanEva.py --input-dir path_to_HumanEva --output-dir ./data/HumanEva_60frame  --seq-len 60

• Then, run this script to evaluate the motion denoising task on all sub-sequences in the data-dir:

  python -m run.motion_denoising --config configs/subvp/amass_scorefc_continuous.py --data-dir ./data/HumanEva_60frame --noise-std 0.04

Human Mesh Recovery

To test on the EHF dataset, follow these steps:

• First, download the EHF dataset from SMPLX.
• Specify the --data-dir and run this script:

  python -m run.fitting --config configs/subvp/amass_scorefc_continuous.py --data-dir path_to_EHF --outdir ./fitting_results

❓ Troubleshoots

• RuntimeError: Subtraction, the '-' operator, with a bool tensor is not supported. If you are trying to invert a mask, use the '~' or 'logical_not()' operator instead.: Solution here

• TypeError: startswith first arg must be bytes or a tuple of bytes, not str.: Fix here.

🙏 Acknowledgement

Big thanks to ScoreSDE, GFPose, and Hand4Whole for their foundational work and code.

📚 Reference

@article{lu2023dposer,
  title={DPoser: Diffusion Model as Robust 3D Human Pose Prior},
  author={Lu, Junzhe and Lin, Jing and Dou, Hongkun and Zhang, Yulun and Deng, Yue and Wang, Haoqian},
  journal={arXiv preprint arXiv:2312.05541},
  year={2023}
}