We present GSwap, a novel consistent and realistic video head-swapping system empowered by dynamic neural Gaussian portrait priors, which significantly advances the state of the art in face and head replacement. Unlike previous methods that rely primarily on 2D generative models or 3D Morphable Face Models (3DMM), our approach overcomes their inherent limitations, including poor 3D consistency, unnatural facial expressions, and restricted synthesis quality. Moreover, existing techniques struggle with full head-swapping tasks due to insufficient holistic head modeling and ineffective background blending, often resulting in visible artifacts and misalignments. To address these challenges, GSwap introduces an intrinsic 3D Gaussian feature field embedded within a full-body SMPL-X surface, effectively elevating 2D portrait videos into a dynamic neural Gaussian field. This innovation ensures high-fidelity, 3D-consistent portrait rendering while preserving natural head-torso relationships and seamless motion dynamics. To facilitate training, we adapt a pretrained 2D portrait generative model to the source head domain using only a few reference images, enabling efficient domain adaptation. Furthermore, we propose a neural re-rendering strategy that harmoniously integrates the synthesized foreground with the original background, eliminating blending artifacts and enhancing realism. Extensive experiments demonstrate that GSwap surpasses existing methods in multiple aspects, including visual quality, temporal coherence, identity preservation, and 3D consistency.

翻译：我们提出GSwap，一种新颖的、具有一致性与真实感的视频换头系统。该系统利用动态神经高斯肖像先验，显著推动了面部与头部替换技术的最前沿发展。不同于以往主要依赖二维生成模型或三维形变人脸模型（3DMM）的方法，我们的方法克服了它们的固有局限，包括较差的三维一致性、不自然的面部表情以及受限的合成质量。此外，现有技术在进行完整换头任务时，由于缺乏整体的头部建模和无效的背景融合，常常导致可见伪影与错位。为应对这些挑战，GSwap在全身SMPL-X表面嵌入了一个内在的三维高斯特征场，有效将二维肖像视频提升为动态神经高斯场。这一创新确保了高保真、三维一致的肖像渲染，同时保留了自然的头-躯干关系和流畅的运动动态。为便于训练，我们仅使用少量参考图像将预训练的二维肖像生成模型适配至源头部领域，实现了高效的领域适配。此外，我们提出一种神经重渲染策略，该策略将合成的前景与原始背景和谐融合，消除了融合伪影并增强了真实感。大量实验表明，GSwap在视觉质量、时间连贯性、身份保持及三维一致性等多个方面均超越了现有方法。