  Zero-shot novel view synthesis (NVS) from a single image is an essential
problem in 3D object understanding. While recent approaches that leverage
pre-trained generative models can synthesize high-quality novel views from
in-the-wild inputs, they still struggle to maintain 3D consistency across
different views. In this paper, we present Consistent-1-to-3, which is a
generative framework that significantly mitigates this issue. Specifically, we
decompose the NVS task into two stages: (i) transforming observed regions to a
novel view, and (ii) hallucinating unseen regions. We design a scene
representation transformer and view-conditioned diffusion model for performing
these two stages respectively. Inside the models, to enforce 3D consistency, we
propose to employ epipolor-guided attention to incorporate geometry
constraints, and multi-view attention to better aggregate multi-view
information. Finally, we design a hierarchy generation paradigm to generate
long sequences of consistent views, allowing a full 360-degree observation of
the provided object image. Qualitative and quantitative evaluation over
multiple datasets demonstrates the effectiveness of the proposed mechanisms
against state-of-the-art approaches. Our project page is at
https://jianglongye.com/consistent123/
