Year: 2025
Type: conference-paper
Abstract: Existing methods like Neural Radiation Fields (NeRF) and 3D Gaussian Splatting (3DGS) have made significant strides in facial attribute control such as facial animation and components editing, yet they struggle with fine-grained representation and scalability in dynamic head modeling. To address these limitations, we propose GaussianStyle. a novel framework that integrates the volumetric strengths of 3DGS with the powerful implicit representation of Style-GAN. The GaussianStyle preserves structural information, such as expressions and poses, using Gaussian points, while projecting the implicit volumetric representation into Style-GAN to capture high-frequency details and mitigate the over-smoothing commonly observed in neural texture rendering. Experimental outcomes indicate that our method achieves state-of-the-art performance in reenactment, novel view synthesis, and animation.
Authors: Pinxin Liu, Luchuan Song, Daoan Zhang, Yunlong Tang, Hang Hua, Huaijin Tu, Jiebo Luo, Chenliang Xu
Institutions: University of Rochester, Georgia Institute of Technology
Language: English
Subfield: Computational Mechanics
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4413559173