HumanReg: Self-supervised Non-rigid Registration of Human Point Cloud
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866909094697238528 |
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| author | Chen, Yifan Pan, Zhiyu Zhong, Zhicheng Guo, Wenxuan Feng, Jianjiang Zhou, Jie |
| author_facet | Chen, Yifan Pan, Zhiyu Zhong, Zhicheng Guo, Wenxuan Feng, Jianjiang Zhou, Jie |
| contents | In this paper, we present a novel registration framework, HumanReg, that learns a non-rigid transformation between two human point clouds end-to-end. We introduce body prior into the registration process to efficiently handle this type of point cloud. Unlike most exsisting supervised registration techniques that require expensive point-wise flow annotations, HumanReg can be trained in a self-supervised manner benefiting from a set of novel loss functions. To make our model better converge on real-world data, we also propose a pretraining strategy, and a synthetic dataset (HumanSyn4D) consists of dynamic, sparse human point clouds and their auto-generated ground truth annotations. Our experiments shows that HumanReg achieves state-of-the-art performance on CAPE-512 dataset and gains a qualitative result on another more challenging real-world dataset. Furthermore, our ablation studies demonstrate the effectiveness of our synthetic dataset and novel loss functions. Our code and synthetic dataset is available at https://github.com/chenyifanthu/HumanReg. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_05462 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | HumanReg: Self-supervised Non-rigid Registration of Human Point Cloud Chen, Yifan Pan, Zhiyu Zhong, Zhicheng Guo, Wenxuan Feng, Jianjiang Zhou, Jie Computer Vision and Pattern Recognition In this paper, we present a novel registration framework, HumanReg, that learns a non-rigid transformation between two human point clouds end-to-end. We introduce body prior into the registration process to efficiently handle this type of point cloud. Unlike most exsisting supervised registration techniques that require expensive point-wise flow annotations, HumanReg can be trained in a self-supervised manner benefiting from a set of novel loss functions. To make our model better converge on real-world data, we also propose a pretraining strategy, and a synthetic dataset (HumanSyn4D) consists of dynamic, sparse human point clouds and their auto-generated ground truth annotations. Our experiments shows that HumanReg achieves state-of-the-art performance on CAPE-512 dataset and gains a qualitative result on another more challenging real-world dataset. Furthermore, our ablation studies demonstrate the effectiveness of our synthetic dataset and novel loss functions. Our code and synthetic dataset is available at https://github.com/chenyifanthu/HumanReg. |
| title | HumanReg: Self-supervised Non-rigid Registration of Human Point Cloud |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2312.05462 |