发表机构
Monash University; College of Computer Science, Chongqing University（莫纳什大学; 重庆大学计算机学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
Fresco++是一种用于细粒度头部化身重建的统一优化框架，通过频率课程与规范共识解决逐视图监督导致的细节不稳定和跨视图不一致问题，在NeRSemble上验证了其重建质量与一致性的提升及通用性。
AI 中文摘要
我们提出了Fresco++，一种用于细粒度和视图一致的头部化身重建的统一优化框架。头部化身优化通常由逐视图图像监督驱动，这可能导致不稳定的高频细节过早拟合以及不同视点间的局部外观不一致。Fresco++通过在优化过程中同时调节视觉细节的演进和跨视图监督的形成来应对这些挑战。对于频率感知优化，渐进式课程首先稳定低频结构，然后引入高频约束以恢复精细的面部和头发细节，同时避免在早期阶段放大虚假响应。对于跨视图优化，我们引入了规范组共识，该共识通过共享的规范表面区域关联局部观测并在不同视点间建立对应关系。几何和可见性感知筛选会移除不可靠的观测，而剩余的多视图证据会在特征空间中聚合，以形成监督当前渲染的共识目标。这种设计无需依赖特定的图像空间参数化即可强制局部一致性，且避免了辅助视图的额外渲染。频率课程与规范共识相结合，实现了从粗结构到细细节的稳定优化，同时保持了跨视点的连贯外观。在NeRSemble上进行的大量实验表明，重建质量和跨视图一致性得到了提升，对不同化身表示的评估进一步证实了Fresco++的通用性和可迁移性。
英文摘要
We propose Fresco++, a unified optimization framework for fine-grained and view-consistent head avatar reconstruction. Head avatar optimization is typically driven by per-view image supervision, which can lead to premature fitting of unstable high-frequency details and inconsistent local appearance across viewpoints. Fresco++ addresses these challenges by regulating both the progression of visual detail and the formation of cross-view supervision during optimization. For frequency-aware optimization, a progressive curriculum first stabilizes low-frequency structures and then introduces high-frequency constraints to recover fine facial and hair details without amplifying spurious responses at early stages. For cross-view optimization, we introduce Canonical Group Consensus, which associates local observations through shared canonical surface regions and establishes correspondence across different viewpoints. Geometric and visibility-aware screening removes unreliable observations, while the remaining multi-view evidence is aggregated in feature space to form a consensus target for supervising the current rendering. This design enforces local consistency without relying on a specific image-space parameterization and avoids additional rendering of the auxiliary view. Together, the frequency curriculum and canonical consensus provide stable optimization from coarse structures to fine details while maintaining coherent appearance across viewpoints. Extensive experiments on NeRSemble demonstrate improved reconstruction quality and cross-view consistency, while evaluations across diverse avatar representations further confirm the generality and transferability of Fresco++.
Comments14 pages, 10 figures
