UniGS: Unified 3D Gaussian Splatting for Long-Haired Talking Portraits

Talking portrait synthesis is a crucial task in computer vision, enabling realistic animations for applications in virtual communication, entertainment, and digital media. Current methods primarily focus on short-hair scenarios, where they rely on rigid segmentation to separate the head from the torso, followed by head reconstruction and simple compositional strategies to combine the head back with the torso. However, these methods face significant challenges when applied to long-haired individuals due to the complex interactions between hair and body, which can lead to visual artifacts and misalignments. In this work, we introduce a novel dataset specifically designed for long-haired individuals, providing a comprehensive benchmark for evaluating head-torso separation in these complex scenarios. Building upon this dataset, we propose UniGS, a unified 3DGS-based framework that holistically models the full portrait, eliminating the need for explicit segmentation. By incorporating audio, eye, and pose features into a deformation network and utilizing a static-to-dynamic training strategy, our method achieves superior realism and coherence. Experimental results show that our approach outperforms existing state-of-the-art techniques in both visual quality and inference efficiency, and effectively handles the complex visual challenges posed by long-haired scenarios. Additional comparisons on existing short-hair datasets further confirm the robustness of our method.