ELITE: 通过学习初始化和测试时生成适应从单目视频高效生成高斯头人偶
ELITE: Efficient Gaussian Head Avatar from a Monocular Video via Learned Initialization and TEst-time Generative Adaptation
AI总结：
ELITE通过学习初始化和测试时生成适应，高效生成高斯头人偶，实现高保真和强野外泛化，合成速度比2D方法快60倍。
摘要：
我们介绍了ELITE，一种通过学习初始化和测试时生成适应从单目视频高效生成高斯头人偶的方法。先前的工作要么依赖于3D数据先验，要么依赖于2D生成先验来弥补单目视频中缺失的视觉线索。然而，3D数据先验方法在野外泛化能力较差，而2D生成先验方法计算成本高且容易产生身份幻觉。我们识别了这两种先验之间的互补协同作用，并设计了一种高效的系统，实现了高保真的可动画人偶合成，并具有强大的野外泛化能力。具体来说，我们引入了一个前馈的Mesh2Gaussian先验模型（MGPM），该模型能够快速初始化高斯人偶。为了进一步弥合测试时的领域差距，我们设计了一个测试时生成适应阶段，利用真实和合成图像作为监督。与先前全扩散去噪策略相比，我们提出了一种基于高斯人偶渲染的单步扩散增强器，用于恢复缺失的视觉细节。我们的实验表明，ELITE产生的视觉效果优于先前的工作，即使在具有挑战性的表情情况下，也能实现比2D生成先验方法快60倍的合成速度。
英文摘要：
We introduce ELITE, an Efficient Gaussian head avatar synthesis from a monocular video via Learned Initialization and TEst-time generative adaptation. Prior works rely either on a 3D data prior or a 2D generative prior to compensate for missing visual cues in monocular videos. However, 3D data prior methods often struggle to generalize in-the-wild, while 2D generative prior methods are computationally heavy and prone to identity hallucination. We identify a complementary synergy between these two priors and design an efficient system that achieves high-fidelity animatable avatar synthesis with strong in-the-wild generalization. Specifically, we introduce a feed-forward Mesh2Gaussian Prior Model (MGPM) that enables fast initialization of a Gaussian avatar. To further bridge the domain gap at test time, we design a test-time generative adaptation stage, leveraging both real and synthetic images as supervision. Unlike previous full diffusion denoising strategies that are slow and hallucination-prone, we propose a rendering-guided single-step diffusion enhancer that restores missing visual details, grounded on Gaussian avatar renderings. Our experiments demonstrate that ELITE produces visually superior avatars to prior works, even for challenging expressions, while achieving 60x faster synthesis than the 2D generative prior method.
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