发表机构
KAIST（韩国科学技术院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对多视角扩散模型无法利用镜面反射内容的问题，提出无训练的Ref-GeNVS方法，通过两阶段生成技术实现反射一致的新视角合成，性能优于现有同类方法。
AI 中文摘要
我们提出了Ref-GeNVS，一种用于镜面场景生成式新视角合成（NVS）的无训练、感知反射方法。现有多视角扩散模型常无法识别场景中的镜面，也无法利用反射内容进行场景生成。为在无需额外训练的情况下解决该问题，核心思路是将镜面图像视为两个互补视角。从输入图像中，我们估计镜面平面并反射相机位姿以形成虚拟视角。基于该虚拟视角设置，我们提出由镜面门控注意力和反射注入组成的两阶段生成方法，通过在多视角扩散模型中显式利用反射关系，实现反射一致的NVS。Ref-GeNVS继承了多视角扩散主干的强泛化性，无需微调。在包含镜面的合成与真实场景上，Ref-GeNVS优于近期生成式NVS方法，可生成反射一致、上下文连贯的新视角，揭示仅通过镜面可见的场景结构。项目页面：this https URL
英文摘要
We propose Ref-GeNVS, a training-free, reflection-aware method for generative novel view synthesis (NVS) in mirror scenes. Existing multi-view diffusion models often fail to recognize the mirror in the scene and cannot exploit reflected content for scene generation. To fix this issue without additional training, our key idea is to treat a mirror image as two complementary views. From input images, we estimate the mirror plane and reflect camera poses to form virtual views. Based on this virtual view setup, we propose a two-stage generation method consisting of Mirror-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 of the multi-view diffusion backbone, while it does not require finetuning. On synthetic and real scenes including mirrors, Ref-GeNVS outperforms recent generative NVS methods by generating reflection-consistent and contextually coherent novel views, revealing scene structure visible only through mirrors. Project page: https://kim-geonu.github.io/Ref-GeNVS/
CommentsECCV2026, Project page: https://kim-geonu.github.io/Ref-GeNVS/
