HAFMat: Hybrid Priors Guided Adaptive Fusion for Single-Image Human Material Estimation
Researchers have introduced HAFMat, a novel framework designed to improve the estimation of physically based rendering (PBR) materials from single human images. This method addresses the inherent ambiguity in such estimations by employing a Multi-layer Adaptive Feature Fusion Mechanism. This mechanism adaptively integrates various guidance cues, including appearance, body geometry, and semantic information, at different stages of the decoding process. Experiments show that HAFMat achieves state-of-the-art results on both synthetic and real-world data for material estimation and subsequent relighting tasks. AI
IMPACT This research advances material estimation techniques, potentially improving digital human rendering and virtual content creation.