ZeroNVS: Zero-Shot 360-Degree View Synthesis from a Single Image

Understand

We introduce a 3D-aware diffusion model, ZeroNVS, for single-image novel view synthesis for in-the-wild scenes.

• While existing methods are designed for single objects with masked backgrounds, we propose new techniques to address challenges introduced by in-the-wild multi-object scenes with complex backgrounds.
• Specifically, we train a generative prior on a mixture of data sources that capture object-centric, indoor, and outdoor scenes.
• To address issues from data mixture such as depth-scale ambiguity, we propose a novel camera conditioning parameterization and normalization scheme.