honglinchu / bidrn

PyTorch code for our paper "Binarized Dual Residual Network for 3D Whole-body Human Mesh Recovery"

BiDRN: Binarized Dual Residual Network for 3D Whole-body Human Mesh Recovery

Zhiteng Li, Yulun Zhang, Jing Lin, Haotong Qin, Jinjin Gu, Xin Yuan, Linghe Kong, and Xiaokang Yang, "Binarized 3D Whole-body Human Mesh Recovery", arXiv, 2023

[arXiv] [supplementary material] [visual results] [pretrained models]

Abstract: 3D whole-body human mesh recovery aims to reconstruct the 3D human body, face, and hands from a single image. Although powerful deep learning models have achieved accurate estimation in this task, they require enormous memory and computational resources. Consequently, these methods can hardly be deployed on resource-limited edge devices. In this work, we propose a Binarized Dual Residual Network (BiDRN), a novel quantization method to estimate the 3D human body, face, and hands parameters efficiently. Specifically, we design a basic unit Binarized Dual Residual Block (BiDRB) composed of Local Convolution Residual (LCR) and Block Residual (BR), which can preserve full-precision information as much as possible. For LCR, we generalize it to four kinds of convolutional modules so that full-precision information can be propagated even between mismatched dimensions. We also binarize the face and hands box-prediction network as Binaried BoxNet, which can further reduce the model redundancy. Comprehensive quantitative and qualitative experiments demonstrate the effectiveness of BiDRN, which has a significant improvement over state-of-the-art binarization algorithms. Moreover, our proposed BiDRN achieves comparable performance with full-precision method Hand4Whole while using just 22.1% parameters and 14.8% operations. We will release all the code and pretrained models.

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Image	Hand4Whole (full-precision)	BNN	BiDRN (ours)
Params(M) / OPs (G)	77.84 / 16.85	21.61 / 2.63	17.22 / 2.50

⚒️ TODO

• Complete this repository

🔗 Contents

• Datasets
• Training
• Testing
• Results
• Citation
• Acknowledgements

🔎 Results

We outperform existing state-of-the-art BNN-based methods and even achieve comparable performance with full-precision methods. More results can be found in the paper.

Quantitative Comparison

• results in Table 1 of the main paper

Visual Comparison

• results in Figure 7 of the main paper

Citation

If you find the code helpful in your research or work, please cite the following paper(s).

@article{li2023binarized,
      title={Binarized 3D Whole-body Human Mesh Recovery}, 
      author={Zhiteng Li and Yulun Zhang and Jing Lin and Haotong Qin and Jinjin Gu and Xin Yuan and Linghe Kong and Xiaokang Yang},
      year={2023},
      eprint={2311.14323},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

💡 Acknowledgements

This work is released under the Apache 2.0 license. The codes are based on Hand4Whole. Please also follow their licenses. Thanks for their awesome works.