Most 3D generation research focuses on up-projecting 2D foundation models
into the 3D space, either by minimizing 2D Score Distillation Sampling (SDS)
loss or fine-tuning on multi-view datasets. Without explicit 3D priors, these
methods often lead to geometric anomalies and multi-view inconsistency.
Recently, researchers have attempted to improve the genuineness of 3D objects
by directly training on 3D datasets, albeit at the cost of low-quality texture
generation due to the limited texture diversity in 3D datasets. To harness the
advantages of both approaches, we propose Bidirectional Diffusion(BiDiff), a
unified framework that incorporates both a 3D and a 2D diffusion process, to
preserve both 3D fidelity and 2D texture richness, respectively. Moreover, as a
simple combination may yield inconsistent generation results, we further bridge
them with novel bidirectional guidance. In addition, our method can be used as
an initialization of optimization-based models to further improve the quality
of 3D model and efficiency of optimization, reducing the generation process
from 3.4 hours to 20 minutes. Experimental results have shown that our model
achieves high-quality, diverse, and scalable 3D generation. Project website:
this https URL

以 Bidirectional Diffusion（BiDiff）为框架，同时整合 3D 和 2D 扩散过程，既保持了 3D 的真实性，又保留了 2D 纹理的丰富性，通过新颖的双向引导进一步提高一致性，将生成的过程从 3.4 小时减少到 20 分钟，以达到高质量、多样性和可伸缩性的 3D 生成。