GeoEdit: Geometry-Aware Object Editing via Dual-Branch Denoising

Precisely manipulating objects in a single photograph (trans-lation, rotation, scaling) while obeying 3D physical constraints remainsunsolved for diffusion-based editors. Current 2D methods lack spatialawareness and produce perspective violations. Forcing structural proxiesinto the latent space also disrupts variance homogeneity, and the result-ing self-attention leakage leads to ghosting and background blur. Thecore difficulty is asymmetric: the relocated object must follow a rigidgeometry, yet the uncovered background needs freedom to synthesizeplausible content. We present GeoEdit, a training-free Lift-Manipulate-Render-Denoise pipeline that satisfies both constraints. We decouplescene and object in 3D, align them through point correspondence, andrender a geometry-aligned proxy with a structural depth map. A Dual-Branch Denoising stage then refines this proxy: a video diffusion back-bone preserves object identity, while 3D constraints are injected into theforeground within a narrow denoising window at matching noise vari-ance (variance-homogeneous injection). The background denoises freely.Because the injected signal matches the native latent statistics, self-attention stays undisturbed. We also introduce GeoEditBench, a pose-aware benchmark covering object translation, object rotation, and cam-era movement with pose-aware evaluation metrics. Experiments confirmconsistent gains in geometric accuracy, identity fidelity, and backgroundquality, validated by automatic metrics and human studies. Code anddata are publicly available at https://github.com/Heey731/GeoEdit.