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--- |
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license: cc-by-nc-sa-4.0 |
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license_link: LICENSE |
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language: |
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- en |
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tags: |
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- Synthetic Data |
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- 3D Humans |
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- Human Pose and Shape Estimation |
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- Human NeRF |
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pretty_name: SynBody |
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size_categories: |
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- 100B<n<1T |
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--- |
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# SynBody: Synthetic Dataset with Layered Human Models for 3D Human Perception and Modeling |
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[Homepage](https://synbody.github.io/) |
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Abstract: Synthetic data has emerged as a promising source for 3D human research as it offers low-cost access to large-scale human datasets. |
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To advance the diversity and annotation quality of human models, we introduce a new synthetic dataset, SynBody, with three appealing features: |
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1) a clothed parametric human model that can generate a diverse range of subjects; |
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2) the layered human representation that naturally offers high-quality 3D annotations to support multiple tasks; |
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3) a scalable system for producing realistic data to facilitate real-world tasks. |
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The dataset comprises 1.2M images with corresponding accurate 3D annotations, covering 10,000 human body models, 1,187 actions, and various viewpoints. |
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The dataset includes two subsets for human pose and shape estimation as well as human neural rendering. Extensive experiments on SynBody indicate that |
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it substantially enhances both SMPL and SMPL-X estimation. Furthermore, the incorporation of layered annotations offers a valuable training resource |
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for investigating the Human Neural Radiance Fields (NeRF). |
