GSFaceMorpher: High-Fidelity 3D Face Morphing via Gaussian Splatting

Abstract

High-fidelity 3D face morphing aims to achieve seamless transitions between realistic 3D facial representations of different identities. Although 3D Gaussian Splatting (3DGS) excels in high-quality rendering, its application to morphing is hindered by the lack of Gaussian primitive correspondence and variations in primitive quantities. To address this, we propose GSFaceMorpher, which is a novel framework for high-fidelity 3D face morphing based on 3DGS. Our method constructs an auxiliary model that bridges the source and target face models by aligning the geometry through Radial Basis Function (RBF) warping and optimizing the appearance in the image space. This auxiliary model enables smooth parameter interpolation, whereas a diffusion-based refinement step enhances critical facial details through attention replacement from the reference faces. Experiments demonstrate that our method produces visually coherent and high-fidelity morphing sequences, significantly outperforming NeRF-based baselines in terms of both quantitative metrics and user preferences. Our work establishes a new benchmark for high-fidelity 3D face morphing with applications in visual effects, animation, and immersive experiences.