FATE：基于单目视频的带纹理编辑的全头高斯化身
FATE: Full-head Gaussian Avatar with Textural Editing from Monocular Video
AI总结：
针对单目视频3D头部化身重建存在的不完整、高斯表示效率低等问题，提出FATE方法，通过采样稠密化、神经烘焙和通用补全框架，实现可编辑、可驱动的360°全头重建，性能达当前最优。
摘要：
从易于采集的单目视频中重建高保真、可驱动的3D头部化身是一项关键且极具挑战性的任务。尽管在渲染性能和操控能力方面已取得显著进展，但仍存在重建不完整、高斯表示效率低下等突出难题。为解决这些问题，我们提出FATE——一种从单段单目视频重建可编辑全头化身的新方法。FATE融合了基于采样的稠密化策略，确保点的最优位置分布，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，便于直观的外观编辑；此外还提出通用补全框架，恢复非正面外观，最终实现可360°渲染的3D头部化身。FATE在定性和定量评估中均优于现有方法，达到当前最优性能。据我们所知，FATE是首个可驱动、支持360°全头的单目3D头部化身重建方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
