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The paper introduces UniGeo, a framework for camera-controllable image editing that unifies geometric guidance across representation, architecture, and loss function levels to improve cross-view consistency. UniGeo uses a frame-decoupled geometric reference injection, geometric anchor attention, and trajectory-endpoint geometric supervision to address limitations of existing methods that rely on fragmented geometric guidance and image diffusion models. Experiments show UniGeo significantly outperforms existing methods in visual quality and geometric consistency across various camera motion settings.
By unifying geometric guidance across representation, architecture, and loss, UniGeo achieves unprecedented camera control and cross-view consistency in image editing, leaving previous fragmented approaches in the dust.
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.
