IE-aware Consistency Losses for Detailed 3D Face Reconstruction from Multiple Images in the Wild

Weilong Peng, Yi Luo, Keke Tang, Kongyang Chen, Yangtao Wang, Ping Li, Meie Fang

Abstract: 3D face reconstruction from multiple in-the-wild images in an unsupervised manner poses a significant challenge, primarily due to the pervasive presence of Intrinsic and Extrinsic inconsistencies in facial features. To tackle this, we introduce a novel set of IE-aware consistency losses designed to effectively mitigate these inconsistencies. Our Local Alignment Loss employs neighborhood search techniques to identify and optimize consistent pixel information, thereby reducing intrinsic inconsistencies. In parallel, our Region Subset Selection Loss filters out regions where significant discrepancies exist between the input and reconstructed images, effectively alleviating extrinsic inconsistencies. Extensive experimental results validate the effectiveness of our IE-aware consistency losses in reconstructing detailed 3D facial geometry from images captured in uncontrolled environments.