Reflection-aware generative novel view synthesis

We propose Ref-GeNVS, a training-free, reflection-awaremethod for generative novel view synthesis (NVS) in mirror scenes. Ex-isting multi-view diffusion models often fail to recognize the mirror in thescene and cannot exploit reflected content for scene generation. To fix thisissue without additional training, our key idea is to treat a mirror imageas two complementary views. From input images, we estimate the mirrorplane and reflect camera poses to form virtual views. Based on this vir-tual view setup, we propose a two-stage generation method consisting ofMirror-gated attention and Reflection injection, which enables reflection-consistent NVS by explicitly leveraging reflection relationships in a multi-view diffusion model. Ref-GeNVS inherits the strong generalizability ofthe multi-view diffusion backbone, while it does not require finetuning.On synthetic and real scenes including mirrors, Ref-GeNVS outperformsrecent generative NVS methods by generating reflection-consistent andcontextually coherent novel views, revealing scene structure visible onlythrough mirrors. Project page: https://kim-geonu.github.io/Ref-GeNVS/