UniGeo:通过视频模型统一几何引导以实现相机可控图像编辑
阅读原文· arxiv.org针对现有相机可控图像编辑方法因几何引导碎片化导致的几何漂移与结构退化问题,研究团队提出了UniGeo新框架。该框架利用视频模型提供连续视角先验,并首次在表征、架构和损失函数三个层级系统性地统一注入几何引导。具体创新包括:表征层的帧解耦几何参考注入、架构层的几何锚点注意力对齐多视图特征,以及损失函数层的轨迹端点几何监督策略。在多个公开基准测试中,UniGeo在广泛及有限的相机运动设置下,于视觉质量和几何一致性方面均显著优于现有方法。
Camera-controllable image editing aims to synthesize novel views of a given scene under varying camera poses while strictly preserving cross-view geometric consistency. However, existing methods typically rely on fragmented geometric guidance, such as only injecting point clouds at the representation level despite models containing multiple levels, and are mainly based on image diffusion models that operate on discrete view mappings. These two limitations jointly lead to geometric drift and structural degradation under continuous camera motion. We observe that while leveraging video models provides continuous viewpoint priors for camera-controllable image editing, they still struggle to form stable geometric understanding if geometric guidance remains fragmented. To systematically address this, we inject unified geometric guidance across three levels that jointly determine the generative output: representation, architecture, and loss function. To this end, we propose UniGeo, a novel camera-controllable editing framework. Specifically, at the representation level, UniGeo incorporates a frame-decoupled geometric reference injection mechanism to provide robust cross-view geometry context. At the architecture level, it introduces geometric anchor attention to align multi-view features. At the loss function level, it proposes a trajectory-endpoint geometric supervision strategy to explicitly reinforce the structural fidelity of target views. Comprehensive experiments across multiple public benchmarks, encompassing both extensive and limited camera motion settings, demonstrate that UniGeo significantly outperforms existing methods in both visual quality and geometric consistency.