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 3D-ReGen, 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. 3D-ReGen uses a new conditioning mechanism based on VecSet, which allows the regenerator to update or improve the input geometry with consistent fine-grained details. 3D-ReGen 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 3D-ReGen, achieving state-of-the-art performance in controllable 3D generation across several tasks.

翻译：我们研究了从二维图像和初始三维形状重建三维对象的问题。大多数三维生成器采用一次性方式运行，将文本或图像转换为可控性有限的三维对象。为此，我们提出3D-ReGen——一种以初始三维形状为条件的三维重建器。这一概念上简单的公式使我们能够支持多种实用任务，包括三维增强、三维重建和三维编辑。3D-ReGen采用基于VecSet的新型条件机制，使重建器能够以一致的精细细节更新或改进输入几何结构。通过自监督预任务和数据增强，3D-ReGen从现成的三维数据集中学习广泛适用的重建先验，无需额外标注。我们从几何一致性和精细质量两方面评估了3D-ReGen，在多项任务的受控三维生成中达到了最先进性能。