SpatialAvatar-0：采用多阶段重建的高质量4D头部化身

摘要

高质量4D头部虚拟形象仅需一张或少数源肖像，是远程临场、增强现实/虚拟现实以及数字人交互的核心技术。3D高斯散点法（3DGS）已成为主导性表征技术，其两条互补路径（可泛化的前馈预测器与逐主体精化器）正并行发展。然而现有前馈预测器仅在单一数据集家族上训练且采用硬编码源数量，继承了相应领域偏差。逐主体精化器需30万至60万次迭代，且依赖自适应稠密化破坏上游高斯布局，导致两条路径无法端到端共享表征。为弥合这一鸿沟，我们提出基于共享FLAME网格绑定高斯表征的SpatialAvatar-0：采用无参数K源均值池化的前馈生成器，配合单目时序到多视角空间的两阶段调度机制，防止身份先验坍塌至较小规模多视角数据集。我们进一步引入10K次迭代的布局保持型逐主体精化循环，冻结FLAME绑定与高斯数量，以三分量抗尖峰正则化替代稠密化。在VFHQ/HDTF跨域零样本测试中，尽管从未在任一测试域训练，我们仍以+1.5 dB PSNR超越域内领先模型GAGAvatar；在SplattingAvatar单目基准测试中，我们在所有报告指标上领先，以比常见SOTA基线快60倍的逐主体调度，在PSNR上超越30万次迭代的GeoAvatar达+1.3 dB。网站：https://spatialwalk.github.io/SpatialAvatar-0。

English

High-quality 4D head avatars from one or a few source portraits are central to telepresence, AR/VR, and digital-human interaction. 3D Gaussian Splatting (3DGS) has emerged as the dominant representation, with two complementary regimes (generalizable feed-forward predictors and per-subject refiners) maturing in parallel. However, existing feed-forward predictors are trained on a single dataset family with a hard-coded source count, inheriting the corresponding domain bias. Per-subject refiners require 300K--600K iterations and rely on adaptive densification that destroys upstream Gaussian layouts, preventing the two regimes from sharing a representation end-to-end. To bridge both regimes we propose SpatialAvatar-0 on a shared FLAME-mesh-bound Gaussian representation: a feed-forward generator with a parameter-free K-source mean-pool and a monocular-temporal to multi-view-spatial two-phase schedule that anchors against identity-prior collapse onto the smaller multi-view set. We further introduce a 10K-iter layout-preserving per-subject refinement loop that freezes the FLAME-binding and Gaussian count and replaces densification with a three-component anti-spike regularization. On VFHQ/HDTF cross-domain zero-shot we surpass the in-domain leader GAGAvatar by +1.5 dB PSNR despite never training on either test domain, and on the SplattingAvatar monocular benchmark we lead every reported metric, surpassing the 300K-iter GeoAvatar by +1.3 dB PSNR at up to 60x shorter per-subject schedule than common SOTA baselines. Website: https://spatialwalk.github.io/SpatialAvatar-0.