UniGeo: Unifying Geometric Guidance for Camera-Controllable Image Editing via Video Models

TL;DR

UniGeo introduces a unified geometric guidance framework for camera-controllable image editing, enhancing structural fidelity and consistency across views by integrating guidance at multiple levels within video models.

Contribution

It systematically unifies geometric guidance at representation, architecture, and loss levels, improving stability and accuracy in camera-controllable image editing.

Findings

Outperforms existing methods in visual quality.

Achieves superior geometric consistency across views.

Effective under both extensive and limited camera motions.

Abstract

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.