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

20 Mar 2019arXiv:1903.08527archive 2025-07-28

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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Microsoft/Deep3DFaceReconstruction officialmentioned in papermentioned on GitHubtfMIT report
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POS Microsoft/Deep3DFaceReconstruction/preprocess_img.py official repository unverified MIT (permissive) · d536967c5e11597f · report
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resize_n_crop_img Microsoft/Deep3DFaceReconstruction/preprocess_img.py official repository unverified MIT (permissive) · 8e794efb786fa179 · report
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Tasks

3D Face ReconstructionFace ReconstructionWeakly-supervised Learning

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
3D Face Reconstruction Florence Deng RMSE Cooperative 1.60 #10 of 16 Archive leaderboard report
3D Face Reconstruction Florence Deng RMSE Indoor 1.61 #10 of 16 Archive leaderboard report
3D Face Reconstruction Florence Deep3DFaceReconstruction RMSE Cooperative 1.66 #12 of 16 Archive leaderboard report
3D Face Reconstruction Florence Deep3DFaceReconstruction RMSE Indoor 1.66 #12 of 16 Archive leaderboard report
3D Face Reconstruction Florence Deep3DFaceReconstruction RMSE Outdoor 1.69 #12 of 16 Archive leaderboard report
3D Face Reconstruction NoW Benchmark Deep3DFaceRecon PyTorch Mean Reconstruction Error (mm) 1.41 #6 of 17 Archive leaderboard report
3D Face Reconstruction NoW Benchmark Deep3DFaceRecon PyTorch Median Reconstruction Error 1.11 #6 of 17 Archive leaderboard report
3D Face Reconstruction NoW Benchmark Deep3DFaceRecon PyTorch Stdev Reconstruction Error (mm) 1.21 #6 of 17 Archive leaderboard report
3D Face Reconstruction NoW Benchmark Deng et al. 2019 Mean Reconstruction Error (mm) 1.54 #10 of 17 Archive leaderboard report
3D Face Reconstruction NoW Benchmark Deng et al. 2019 Median Reconstruction Error 1.23 #10 of 17 Archive leaderboard report
3D Face Reconstruction NoW Benchmark Deng et al. 2019 Stdev Reconstruction Error (mm) 1.29 #10 of 17 Archive leaderboard report
3D Face Reconstruction REALY Deep3D @cheek 1.528 (±0.501) #7 of 24 Archive leaderboard report
3D Face Reconstruction REALY Deep3D @forehead 2.015 (±0.449) #7 of 24 Archive leaderboard report
3D Face Reconstruction REALY Deep3D @mouth 1.368 (±0.439) #7 of 24 Archive leaderboard report
3D Face Reconstruction REALY Deep3D @nose 1.719 (±0.354) #7 of 24 Archive leaderboard report
3D Face Reconstruction REALY Deep3D all 1.657 #7 of 24 Archive leaderboard report
3D Face Reconstruction REALY (side-view) Deep3D @cheek 1.528 (±0.517) #5 of 19 Archive leaderboard report
3D Face Reconstruction REALY (side-view) Deep3D @forehead 2.074 (±0.486) #5 of 19 Archive leaderboard report
3D Face Reconstruction REALY (side-view) Deep3D @mouth 1.411 (±0.395) #5 of 19 Archive leaderboard report
3D Face Reconstruction REALY (side-view) Deep3D @nose 1.749 (±0.343) #5 of 19 Archive leaderboard report
3D Face Reconstruction REALY (side-view) Deep3D all 1.657 #5 of 19 Archive leaderboard report

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