ZeroNVS: Zero-shot 360-degree View Synthesis from a Single Real Image

Kyle Sargent, Zizhang Li, Tanmay Shah, Charles Herrmann, Hong-Xing Yu, Yunzhi Zhang, Eric Ryan Chan, Dmitry Lagun, Li Fei-Fei, Deqing Sun, Jiajun Wu

Keywords: Computer vision, novel view synthesis

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

Abstract: We introduce a 3D-aware diffusion model, ZeroNVS, for single-image novel view synthesis for in-the-wild scenes. Compared with existing methods mainly developed for single objects with masked backgrounds, we propose key improvements to address challenges introduced by in-the-wild scenes with complex backgrounds. Specifically, we train a generative prior on a mixture of data sources that capture object-centric, indoor, and outdoor scenes. As the data mixture presents various issues such as depth-scale ambiguity, we present a novel camera parameterization and normalization scheme. Further, we observe that Score Distillation Sampling (SDS) tends to truncate the distribution of complex backgrounds during distillation of 360º scenes, and propose ``SDS-anchoring'' to improve the diversity of synthesized novel views. Our model sets a new state-of-the-art in LPIPS on DTU in the zero-shot setting, even outperforming methods specifically trained on DTU. We further adapt the challenging MipNeRF360 dataset as a new benchmark for single-image novel view synthesis, and demonstrate strong performance. Our code and data will be available on acceptance.

Primary Area: representation learning for computer vision, audio, language, and other modalities

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Submission Number: 2865