3D Mesh Editing using Masked LRMs
| Autor: | Gao, Will, Wang, Dilin, Fan, Yuchen, Bozic, Aljaz, Stuyck, Tuur, Li, Zhengqin, Dong, Zhao, Ranjan, Rakesh, Sarafianos, Nikolaos |
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| Rok vydání: | 2024 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | We present a novel approach to mesh shape editing, building on recent progress in 3D reconstruction from multi-view images. We formulate shape editing as a conditional reconstruction problem, where the model must reconstruct the input shape with the exception of a specified 3D region, in which the geometry should be generated from the conditional signal. To this end, we train a conditional Large Reconstruction Model (LRM) for masked reconstruction, using multi-view consistent masks rendered from a randomly generated 3D occlusion, and using one clean viewpoint as the conditional signal. During inference, we manually define a 3D region to edit and provide an edited image from a canonical viewpoint to fill in that region. We demonstrate that, in just a single forward pass, our method not only preserves the input geometry in the unmasked region through reconstruction capabilities on par with SoTA, but is also expressive enough to perform a variety of mesh edits from a single image guidance that past works struggle with, while being 10x faster than the top-performing competing prior work. Comment: Project Page: https://chocolatebiscuit.github.io/MaskedLRM/ |
| Databáze: | arXiv |
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