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

Arch 4A-E Poster #448
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Wed 19 Jun 5 p.m. PDT — 6:30 p.m. PDT


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. Further, we observe that Score Distillation Sampling (SDS) tends to truncate the distribution of complex backgrounds during distillation of 360-degree scenes, and propose "SDS anchoring" to improve the diversity of synthesized novel views. Our model sets a new state-of-the-art result in LPIPS on the DTU dataset in the zero-shot setting, even outperforming methods specifically trained on DTU. We further adapt the challenging Mip-NeRF 360 dataset as a new benchmark for single-image novel view synthesis, and demonstrate strong performance in this setting. Code and models are available at

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