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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.14860 |
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| _version_ | 1866914924988465152 |
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| author | Yoshiyasu, Yusuke Sun, Leyuan |
| author_facet | Yoshiyasu, Yusuke Sun, Leyuan |
| contents | This paper presents DiffSurf, a transformer-based denoising diffusion model for generating and reconstructing 3D surfaces. Specifically, we design a diffusion transformer architecture that predicts noise from noisy 3D surface vertices and normals. With this architecture, DiffSurf is able to generate 3D surfaces in various poses and shapes, such as human bodies, hands, animals and man-made objects. Further, DiffSurf is versatile in that it can address various 3D downstream tasks including morphing, body shape variation and 3D human mesh fitting to 2D keypoints. Experimental results on 3D human model benchmarks demonstrate that DiffSurf can generate shapes with greater diversity and higher quality than previous generative models. Furthermore, when applied to the task of single-image 3D human mesh recovery, DiffSurf achieves accuracy comparable to prior techniques at a near real-time rate. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_14860 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DiffSurf: A Transformer-based Diffusion Model for Generating and Reconstructing 3D Surfaces in Pose Yoshiyasu, Yusuke Sun, Leyuan Computer Vision and Pattern Recognition This paper presents DiffSurf, a transformer-based denoising diffusion model for generating and reconstructing 3D surfaces. Specifically, we design a diffusion transformer architecture that predicts noise from noisy 3D surface vertices and normals. With this architecture, DiffSurf is able to generate 3D surfaces in various poses and shapes, such as human bodies, hands, animals and man-made objects. Further, DiffSurf is versatile in that it can address various 3D downstream tasks including morphing, body shape variation and 3D human mesh fitting to 2D keypoints. Experimental results on 3D human model benchmarks demonstrate that DiffSurf can generate shapes with greater diversity and higher quality than previous generative models. Furthermore, when applied to the task of single-image 3D human mesh recovery, DiffSurf achieves accuracy comparable to prior techniques at a near real-time rate. |
| title | DiffSurf: A Transformer-based Diffusion Model for Generating and Reconstructing 3D Surfaces in Pose |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.14860 |