  3D content generation has wide applications in various fields. One of its
dominant paradigms is by sparse-view reconstruction using multi-view images
generated by diffusion models. However, since directly reconstructing triangle
meshes from multi-view images is challenging, most methodologies opt to an
implicit representation (such as NeRF) during the sparse-view reconstruction
and acquire the target mesh by a post-processing extraction. However, the
implicit representation takes extensive time to train and the post-extraction
also leads to undesirable visual artifacts. In this paper, we propose
FlexiDreamer, a novel framework that directly reconstructs high-quality meshes
from multi-view generated images. We utilize an advanced gradient-based mesh
optimization, namely FlexiCubes, for multi-view mesh reconstruction, which
enables us to generate 3D meshes in an end-to-end manner. To address the
reconstruction artifacts owing to the inconsistencies from generated images, we
design a hybrid positional encoding scheme to improve the reconstruction
geometry and an orientation-aware texture mapping to mitigate surface ghosting.
To further enhance the results, we respectively incorporate eikonal and smooth
regularizations to reduce geometric holes and surface noise. Our approach can
generate high-fidelity 3D meshes in the single image-to-3D downstream task with
approximately 1 minute, significantly outperforming previous methods.
