Title: GaussianMorphing：Mesh-Guided 3D Gaussians for Semantic-Aware Object Morphing
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: 3D Computer Vision

Abstract: We introduce GaussianMorphing, a novel framework for semantic-aware 3D shape and texture morphing from multi-view images. Unlike conventional approaches constrained to point clouds or correspondence-aligned untextured data, our approach leverages mesh-guided 3D Gaussian Splatting (3DGS) to achieve high-fidelity appearance and geometry representation. On the one hand, our unified mesh-guided Gaussian deformation strategy ensures geometrically consistent deformation by binding 3DGS points to reconstructed mesh patches while preserving texture fidelity through topology-aware constraints. On the other hand, the framework establishes unsupervised semantic correspondence by exploiting mesh topology as a geometric prior, while maintaining structural integrity through physically plausible point trajectory constraints. This integrated approach maintains both local geometric details and global semantic coherence throughout the morphing process without requiring labeled data. Experimental results show that GaussianMorphing outperforms prior 2D/3D morphing methods, with a color consistency ($\Delta E$) reduction of  22.2%  and an EI reduction of 26.2%  on our proposed TexMorph.