ObjectMorpher: 3D-Aware Image Editing via Deformable 3DGS Models
This addresses the challenge of 3D-aware image editing for users needing fine-grained, photorealistic control, representing a novel method for a known bottleneck rather than a foundational advance.
The paper tackled the problem of achieving precise, object-level control in image editing by introducing ObjectMorpher, a framework that converts ambiguous 2D edits into geometry-grounded operations using deformable 3D Gaussian Splatting models, resulting in superior performance on metrics like KID, LPIPS, SIFID, and user preference.
Achieving precise, object-level control in image editing remains challenging: 2D methods lack 3D awareness and often yield ambiguous or implausible results, while existing 3D-aware approaches rely on heavy optimization or incomplete monocular reconstructions. We present ObjectMorpher, a unified, interactive framework that converts ambiguous 2D edits into geometry-grounded operations. ObjectMorpher lifts target instances with an image-to-3D generator into editable 3D Gaussian Splatting (3DGS), enabling fast, identity-preserving manipulation. Users drag control points; a graph-based non-rigid deformation with as-rigid-as-possible (ARAP) constraints ensures physically sensible shape and pose changes. A composite diffusion module harmonizes lighting, color, and boundaries for seamless reintegration. Across diverse categories, ObjectMorpher delivers fine-grained, photorealistic edits with superior controllability and efficiency, outperforming 2D drag and 3D-aware baselines on KID, LPIPS, SIFID, and user preference.