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
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. Our code and data are at http://kylesargent.github.io/zeronvs/