仅用单视频重建可编辑的360度全头3D形象，效果更完整高效
FATE: Full-head Gaussian Avatar with Textural Editing from Monocular Video
- 用采样优化点云分布，提升渲染效率
- 将离散高斯转为连续属性图，支持直观纹理编辑
- 首次实现单视频驱动的可动画360度全头重建，适合虚拟人开发
从轻松拍摄的单视角视频中重建高保真、可动画的3D头部形象是一项关键但艰巨的挑战。尽管在渲染质量与操作能力上已取得显著进展，仍存在重建不完整和高斯表示效率低等问题。为此，我们提出FATE，一种从单个单目视频重建可编辑全头形象的新方法。FATE采用基于采样的稠密化策略，确保点云位置分布最优，提升渲染效率；引入神经烘焙技术，将离散高斯表示转换为连续属性图，便于直观外观编辑；此外，提出通用补全框架以恢复非正面视角的外观，最终生成360°可渲染的3D头部形象。FATE在定性和定量评估中均优于以往方法，达到当前最佳性能。据我们所知，FATE是首个可动画且支持360°全头单视频重建的3D头部形象方法。
原文摘要 · Abstract (English)
Reconstructing high-fidelity, animatable 3D head avatars from effortlessly captured monocular videos is a pivotal yet formidable challenge. Although significant progress has been made in rendering performance and manipulation capabilities, notable challenges remain, including incomplete reconstruction and inefficient Gaussian representation. To address these challenges, we introduce FATE, a novel method for reconstructing an editable full-head avatar from a single monocular video. FATE integrates a sampling-based densification strategy to ensure optimal positional distribution of points, improving rendering efficiency. A neural baking technique is introduced to convert discrete Gaussian representations into continuous attribute maps, facilitating intuitive appearance editing. Furthermore, we propose a universal completion framework to recover non-frontal appearance, culminating in a 360$^\circ$-renderable 3D head avatar. FATE outperforms previous approaches in both qualitative and quantitative evaluations, achieving state-of-the-art performance. To the best of our knowledge, FATE is the first animatable and 360$^\circ$ full-head monocular reconstruction method for a 3D head avatar.
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