Fresco: Frequency-Spatial Consistent Optimization for Fine-Grained Head Avatar Modeling
Zhang, Shikun, Li, Yong, Wang, Yiqun, Ke, Qiuhong, Chen, Cunjian
原文摘要
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.
cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表