FlexiDreamer: Single Image-to-3D Generation with FlexiCubes
Authors: name: Ruowen Zhao, name: Zhengyi Wang, name: Yikai Wang, name: Zihan Zhou, name: 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.