ArtFormer: Controllable Generation of Diverse 3D Articulated Objects
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866908297892724736 |
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| author | Su, Jiayi Feng, Youhe Li, Zheng Song, Jinhua He, Yangfan Ren, Botao Xu, Botian |
| author_facet | Su, Jiayi Feng, Youhe Li, Zheng Song, Jinhua He, Yangfan Ren, Botao Xu, Botian |
| contents | This paper presents a novel framework for modeling and conditional generation of 3D articulated objects. Troubled by flexibility-quality tradeoffs, existing methods are often limited to using predefined structures or retrieving shapes from static datasets. To address these challenges, we parameterize an articulated object as a tree of tokens and employ a transformer to generate both the object's high-level geometry code and its kinematic relations. Subsequently, each sub-part's geometry is further decoded using a signed-distance-function (SDF) shape prior, facilitating the synthesis of high-quality 3D shapes. Our approach enables the generation of diverse objects with high-quality geometry and varying number of parts. Comprehensive experiments on conditional generation from text descriptions demonstrate the effectiveness and flexibility of our method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_07237 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ArtFormer: Controllable Generation of Diverse 3D Articulated Objects Su, Jiayi Feng, Youhe Li, Zheng Song, Jinhua He, Yangfan Ren, Botao Xu, Botian Computer Vision and Pattern Recognition Artificial Intelligence Robotics This paper presents a novel framework for modeling and conditional generation of 3D articulated objects. Troubled by flexibility-quality tradeoffs, existing methods are often limited to using predefined structures or retrieving shapes from static datasets. To address these challenges, we parameterize an articulated object as a tree of tokens and employ a transformer to generate both the object's high-level geometry code and its kinematic relations. Subsequently, each sub-part's geometry is further decoded using a signed-distance-function (SDF) shape prior, facilitating the synthesis of high-quality 3D shapes. Our approach enables the generation of diverse objects with high-quality geometry and varying number of parts. Comprehensive experiments on conditional generation from text descriptions demonstrate the effectiveness and flexibility of our method. |
| title | ArtFormer: Controllable Generation of Diverse 3D Articulated Objects |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Robotics |
| url | https://arxiv.org/abs/2412.07237 |