Fresco：面向细粒度头部虚拟人建模的频率-空间一致性优化
Fresco: Frequency-Spatial Consistent Optimization for Fine-Grained Head Avatar Modeling
把拉普拉斯金字塔频率课程与 UV 空间像素级一致性正则结合，用于抑制头部虚拟人重建中的早期过锐化与跨视角漂移；这套「先低频后高频 + 共享 UV 坐标对齐」的优化调度可直接迁移到其他多视角可驱动人体/人脸重建管线。
原文依据 Fresco combines a Laplacian-pyramid-based frequency curriculum with UV-space consistency regularization
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论我们提出 Fresco，一个统一的优化流程，旨在缓解头部虚拟人重建中的早期过锐化与跨视角漂移问题。Fresco 将基于拉普拉斯金字塔的频率课程与 UV 空间一致性正则化相结合，以渐进方式提升重建质量。优化首先在图像域稳定低频外观，从而抑制虚假细节并促进可靠收敛。随着学习推进，通过共享 UV 纹理坐标上的像素级对齐来增强不同视角之间的一致性。最后，在显式频带约束下细化高频分量，并施加接缝边界正则化以保持局部连续性。通过在频率与 UV 对齐的空间中进行优化，Fresco 实现了稳健收敛，不产生伪高频伪影，并在各视角下获得一致的高保真结果。在 NeRSemble 数据集上的实验验证了本设计的有效性，性能优于此前的最优方法。
英文原文摘要 1074 字符
We propose Fresco, a unified optimization pipeline designed to mitigate early over-sharpening, and cross-view drifting in head avatar reconstruction. Fresco combines a Laplacian-pyramid-based frequency curriculum with UV-space consistency regularization to progressively enhance reconstruction quality. The optimization begins by stabilizing low-frequency appearance in the image domain, which suppresses spurious details and promotes reliable convergence. As learning proceeds, consistency across different viewpoints is reinforced through pixel-level alignment on shared UV texture coordinates. Finally, high-frequency components are refined under explicit frequency-band constraints, and seam boundary regularization is applied to preserve local continuity. By optimizing in a frequency- and UV-aligned space, Fresco achieves robust convergence without pseudo high-frequency artifacts and yields consistent, high-fidelity results across views. Experiments on the NeRSemble dataset validate the effectiveness of our design, outperforming previous state-of-the-art methods.