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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.28134 |
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| _version_ | 1866909048212815872 |
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| author | Park, Geon Yeong Shapovalov, Roman Ranjan, Rakesh Ye, Jong Chul Vedaldi, Andrea Nguyen-Phuoc, Thu |
| author_facet | Park, Geon Yeong Shapovalov, Roman Ranjan, Rakesh Ye, Jong Chul Vedaldi, Andrea Nguyen-Phuoc, Thu |
| contents | We consider the problem of regenerating 3D objects from 2D images and initial 3D shapes. Most 3D generators operate in a one-shot fashion, converting text or images to a 3D object with limited controllability. We introduce instead MeshReGen, a 3D regenerator that is conditioned on an initial 3D shape. This conceptually simple formulation allows us to support numerous useful tasks, including 3D enhancement, reconstruction, and editing. MeshReGen uses a new conditioning mechanism based on VecSet, which allows the regenerator to update or improve the input geometry with consistent fine-grained details. MeshReGen learns a widely applicable regeneration prior from off-the-shelf 3D datasets via self-supervised pretext tasks and augmentations, without additional annotations. We evaluate both the geometric consistency and fine-grained quality of MeshReGen, achieving state-of-the-art performance in controllable 3D generation across several tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_28134 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MeshReGen: A Unified 3D Geometry Regeneration Framework Park, Geon Yeong Shapovalov, Roman Ranjan, Rakesh Ye, Jong Chul Vedaldi, Andrea Nguyen-Phuoc, Thu Computer Vision and Pattern Recognition We consider the problem of regenerating 3D objects from 2D images and initial 3D shapes. Most 3D generators operate in a one-shot fashion, converting text or images to a 3D object with limited controllability. We introduce instead MeshReGen, a 3D regenerator that is conditioned on an initial 3D shape. This conceptually simple formulation allows us to support numerous useful tasks, including 3D enhancement, reconstruction, and editing. MeshReGen uses a new conditioning mechanism based on VecSet, which allows the regenerator to update or improve the input geometry with consistent fine-grained details. MeshReGen learns a widely applicable regeneration prior from off-the-shelf 3D datasets via self-supervised pretext tasks and augmentations, without additional annotations. We evaluate both the geometric consistency and fine-grained quality of MeshReGen, achieving state-of-the-art performance in controllable 3D generation across several tasks. |
| title | MeshReGen: A Unified 3D Geometry Regeneration Framework |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.28134 |