Browse State-of-the-Art › Face Alignment

Face Alignment

106 papers with code · 26 benchmarks · 17 datasets archive 2025-07-28

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Face alignment is the task of identifying the geometric structure of faces in digital images, and attempting to obtain a canonical alignment of the face based on translation, scale, and rotation.

( Image credit: 3DDFA_V2 )

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

26 leaderboard tables shown for this task, 26 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted. 10 shown of 26 until expanded.

DatasetBest model (first row in archive order)PaperCodeSyntologyCompare
300W (48 rows) STAR STAR Loss: Reducing Semantic Ambiguity in Facial Landmark Detection code Syntology ran 4 of 4 samples · 0 unverified Compare
WFLW (36 rows) SH-FAN Subpixel Heatmap Regression for Facial Landmark Localization — — Compare
COFW (28 rows) FiFA Fiducial Focus Augmentation for Facial Landmark Detection — — Compare
AFLW-19 (23 rows) FiFA Fiducial Focus Augmentation for Facial Landmark Detection — — Compare
AFLW2000-3D (14 rows) MNN+OR (reannotated) Multi-task head pose estimation in-the-wild code — Compare
WFW (Extra Data) (11 rows) SH-FAN Subpixel Heatmap Regression for Facial Landmark Localization — — Compare
300W Split 2 (7 rows) SPIGA Shape Preserving Facial Landmarks with Graph Attention Networks code — Compare
COFW-68 (7 rows) SPIGA Shape Preserving Facial Landmarks with Graph Attention Networks code — Compare
AFLW2000 (5 rows) MNN+ORB (Reannotated) Multi-task head pose estimation in-the-wild code — Compare
300W Split 2 (300W-LP) (4 rows) SH-FAN Subpixel Heatmap Regression for Facial Landmark Localization — — Compare
COFW-68 (300WLP) (4 rows) SH-FAN Subpixel Heatmap Regression for Facial Landmark Localization — — Compare
FaceScape (4 rows) ASM ASM: Adaptive Skinning Model for High-Quality 3D Face Modeling — — Compare
AFLW (3 rows) SynergyNet Synergy between 3DMM and 3D Landmarks for Accurate 3D Facial Geometry code — Compare
AFLW-LFPA (3 rows) FPN Joint 3D Face Reconstruction and Dense Alignment with Position Map... code Syntology ran 0 of 10 samples · 10 unverified Compare
300W (Common) (2 rows) SPIGA Shape Preserving Facial Landmarks with Graph Attention Networks code — Compare
AFLW-Full (2 rows) Binary Face Alignment Binarized Convolutional Landmark Localizers for Human Pose... code — Compare
IBUG (2 rows) DenseU-Net + Dual Transformer Stacked Dense U-Nets with Dual Transformers for Robust Face Alignment code — Compare
MERL-RAV (2 rows) SPIGA Shape Preserving Facial Landmarks with Graph Attention Networks code — Compare
300-VW (C) (1 row) 2D-FAN How far are we from solving the 2D & 3D Face Alignment problem?... code Syntology ran 4 of 15 samples · 11 unverified Compare
3DFAW (1 row) 3D Face alignment Two-stage Convolutional Part Heatmap Regression for the 1st 3D... code — Compare
AFLW-PIFA (21 points) (1 row) Face alignment Convolutional aggregation of local evidence for large pose face alignment — — Compare
AFLW-PIFA (34 points) (1 row) Face alignment Convolutional aggregation of local evidence for large pose face alignment — — Compare
CelebA + AFLW Unaligned (1 row) Progressive Face SR Progressive Face Super-Resolution via Attention to Facial Landmark code — Compare
CelebA Aligned (1 row) Progressive Face SR Progressive Face Super-Resolution via Attention to Facial Landmark code — Compare
LS3D-W Balanced (1 row) 3D-FAN How far are we from solving the 2D & 3D Face Alignment problem?... code Syntology ran 4 of 15 samples · 11 unverified Compare
Menpo (1 row) LUVLi LUVLi Face Alignment: Estimating Landmarks' Location, Uncertainty,... code — Compare

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

17 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

30 shown of 106 papers with code (308 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

Syntology lines on 14 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.

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