Papers › Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set
Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set
Yu Deng, Jiaolong Yang, Sicheng Xu, Dong Chen, Yunde Jia, Xin Tong
Recently, deep learning based 3D face reconstruction methods have shown promising results in both quality and efficiency.However, training deep neural networks typically requires a large volume of data, whereas face images with ground-truth 3D face shapes are scarce. In this paper, we propose a novel deep 3D face reconstruction approach that 1) leverages a robust, hybrid loss function for weakly-supervised learning which takes into account both low-level and perception-level information for supervision, and 2) performs multi-image face reconstruction by exploiting complementary information from different images for shape aggregation. Our method is fast, accurate, and robust to occlusion and large pose. We provide comprehensive experiments on three datasets, systematically comparing our method with fifteen recent methods and demonstrating its state-of-the-art performance.
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Code
Syntology Ran 15 of 36 code samples harvested from 3 repositories linked to this paper; 21 have no recorded run. Of those that ran: 2 ran · honoured contract; 2 ran · our draft was wrong; 1 ran · fixture could not drive it; 10 ran with no contract checked.
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Code Syntology ran Syntology
36 samples harvested; 15 ran; 2 honoured the contract we drafted; 21 have no recorded run. Read from Syntology's graph 2026-09-25; that is when this build read the record, not when the samples ran.
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