FlexiDreamer: Single Image-to-3D Generation with FlexiCubes
Ruowen Zhao Zhengyi Wang Yikai Wang Zihan Zhou Jun Zhu
3D content generation from text prompts or single images has made remarkable
progress in quality and speed recently. One of its dominant paradigms involves
generating consistent multi-view images followed by a sparse-view
reconstruction. However, due to the challenge of directly deforming the mesh
representation to approach the target topology, most methodologies learn an
implicit representation (such as NeRF) during the sparse-view reconstruction
and acquire the target mesh by a post-processing extraction. Although the
implicit representation can effectively model rich 3D information, its training
typically entails a long convergence time. In addition, the post-extraction
operation from the implicit field also leads to undesirable visual artifacts.
In this paper, we propose FlexiDreamer, a novel single image-to-3d generation
framework that reconstructs the target mesh in an end-to-end manner. By
leveraging a flexible gradient-based extraction known as FlexiCubes, our method
circumvents the defects brought by the post-processing and facilitates a direct
acquisition of the target mesh. Furthermore, we incorporate a multi-resolution
hash grid encoding scheme that progressively activates the encoding levels into
the implicit field in FlexiCubes to help capture geometric details for per-step
optimization. Notably, FlexiDreamer recovers a dense 3D structure from a
single-view image in approximately 1 minute on a single NVIDIA A100 GPU,
outperforming previous methodologies by a large margin.