  Recent 3D large reconstruction models typically employ a two-stage process,
including first generate multi-view images by a multi-view diffusion model, and
then utilize a feed-forward model to reconstruct images to 3D content.However,
multi-view diffusion models often produce low-quality and inconsistent images,
adversely affecting the quality of the final 3D reconstruction. To address this
issue, we propose a unified 3D generation framework called Cycle3D, which
cyclically utilizes a 2D diffusion-based generation module and a feed-forward
3D reconstruction module during the multi-step diffusion process. Concretely,
2D diffusion model is applied for generating high-quality texture, and the
reconstruction model guarantees multi-view consistency.Moreover, 2D diffusion
model can further control the generated content and inject reference-view
information for unseen views, thereby enhancing the diversity and texture
consistency of 3D generation during the denoising process. Extensive
experiments demonstrate the superior ability of our method to create 3D content
with high-quality and consistency compared with state-of-the-art baselines.
