Synthesizing multi-view 3D from one single image is a significant and challenging task. For this goal, Zero-1-to-3 methods aim to extend a 2D latent diffusion model to the 3D scope. These approaches generate the target-view image with a single-view source image and the camera pose as condition information. However, the one-to-one manner adopted in Zero-1-to-3 incurs challenges for building geometric and visual consistency across views, especially for complex objects. We propose a cascade generation framework constructed with two Zero-1-to-3 models, named Cascade-Zero123, to tackle this issue, which progressively extracts 3D information from the source image. Specifically, a self-prompting mechanism is designed to generate several nearby views at first. These views are then fed into the second-stage model along with the source image as generation conditions. With self-prompted multiple views as the supplementary information, our Cascade-Zero123 generates more highly consistent novel-view images than Zero-1-to-3. The promotion is significant for various complex and challenging scenes, involving insects, humans, transparent objects, and stacked multiple objects etc. The project page is at https://cascadezero123.github.io/.

从一张单一图像中综合多视图 3D 是一项重要且具有挑战性的任务。为了解决这个问题，Zero-1-to-3 方法旨在将二维潜在扩散模型推广到三维范围。我们提出了一个由两个 Zero-1-to-3 模型构建的级联生成框架，名为 Cascade-Zero123，用于解决构建视图之间的几何和视觉一致性的问题。利用自发多视图作为补充信息，我们的 Cascade-Zero123 生成比 Zero-1-to-3 更一致的新视图图像，对于各种复杂和具有挑战性的场景非常有效，包括昆虫、人类、透明物体和堆叠多个物体等。

Cascade-Zero123: 单幅图像到高度一致的3D图像的自提近景方法