UniGeo：通过视频模型统一几何指导以实现相机可控的图像编辑
UniGeo: Unifying Geometric Guidance for Camera-Controllable Image Editing via Video Models
AI总结：
UniGeo通过统一的几何指导在三个层面提升相机可控图像编辑的几何一致性与视觉质量，克服传统方法在连续相机运动下的几何漂移和结构退化问题。
摘要：
相机可控的图像编辑旨在在变化的相机姿态下合成给定场景的新视角，同时严格保持跨视角的几何一致性。然而，现有方法通常依赖碎片化的几何指导，如仅在表示层面注入点云，尽管模型包含多个层次，且主要基于操作离散视角映射的图像扩散模型。这两个限制共同导致在连续相机运动下出现几何漂移和结构退化。我们观察到，尽管利用视频模型能为相机可控图像编辑提供连续视角先验，但若几何指导仍碎片化，仍难以形成稳定的几何理解。为此，我们注入统一的几何指导，跨三个层面共同决定生成输出：表示、架构和损失函数。为此，我们提出UniGeo，一种新的相机可控编辑框架。具体而言，在表示层面，UniGeo引入帧解耦的几何参考注入机制，以提供稳健的跨视角几何上下文。在架构层面，它引入几何锚点注意力，以对齐多视角特征。在损失函数层面，它提出轨迹终点几何监督策略，以显式强化目标视角的结构保真度。在多个公开基准上的全面实验，涵盖广泛的和有限的相机运动设置，证明UniGeo在视觉质量和几何一致性上显著优于现有方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
