Papers › Dense Face Alignment
Dense Face Alignment
Yaojie Liu, Amin Jourabloo, William Ren, Xiaoming Liu
Face alignment is a classic problem in the computer vision field. Previous works mostly focus on sparse alignment with a limited number of facial landmark points, i.e., facial landmark detection. In this paper, for the first time, we aim at providing a very dense 3D alignment for large-pose face images. To achieve this, we train a CNN to estimate the 3D face shape, which not only aligns limited facial landmarks but also fits face contours and SIFT feature points. Moreover, we also address the bottleneck of training CNN with multiple datasets, due to different landmark markups on different datasets, such as 5, 34, 68. Experimental results show our method not only provides high-quality, dense 3D face fitting but also outperforms the state-of-the-art facial landmark detection methods on the challenging datasets. Our model can run at real time during testing.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 3D Face Reconstruction | AFLW2000-3D | DeFA | Mean NME | 5.6454% | #7 of 8 | Archive leaderboard | report |
| Face Alignment | AFLW-LFPA | DeFA | Mean NME | 3.86% | #2 of 3 | Archive leaderboard | report |
| Face Alignment | AFLW2000-3D | DeFA | Balanced NME (2D Sparse Alignment) | 4.50% | #12 of 14 | Archive leaderboard | report |
| Face Alignment | AFLW2000-3D | DeFA | Mean NME(3D Dense Alignment) | 6.04% | #12 of 14 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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