  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/.

Computer Vision and Pattern Recognition;Graphics