GOES: 3D Gaussian-based One-shot Head Animation with Any Emotion and Any Style

摘要

Recent advancements in one-shot head avatar generation and animation have garnered significant attention. However, previous works primarily focus on maintaining consistency in expression and pose between the output and driving images, with limited exploration of two crucial factors: emotion and style. In this paper, we introduce GOES, an 3D Gaussian based One-shot head animation framework for any Emotion and any Style. To achieve low rendering consumption and high reenactment speeds, we incorporate 3D Gaussian techniques into our method. Compared to controlling facial emotions with a single label, using an image as the emotion source enables more precise and fine-grained emotional expression modeling. To accurately extract emotion features from any given image, we design an efficient emotion encoder. Based on this module, we employ a deformation predictor to achieve the emotion-driven deformation of facial 3D points. Regarding stylization, directly using style features to control the deformation of 3D Gaussian parameters results in global color changes. However, facial stylization requires region-specific color transformations. To address this, we propose a Global-to-Point mapping network, which maps the global style feature to each 3D Gaussian points. This module enables precise local style adaptation across different regions of the head avatar. Experimental results demonstrate that our approach outperforms existing methods in terms of facial reconstruction quality and expression accuracy, while also supporting customization of arbitrary emotions and styles.