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3D Face Reconstruction

83 papers with code · 9 benchmarks · 11 datasets archive 2025-07-28

Computer Vision

3D Face Reconstruction is a computer vision task that involves creating a 3D model of a human face from a 2D image or a set of images. The goal of 3D face reconstruction is to reconstruct a digital 3D representation of a person's face, which can be used for various applications such as animation, virtual reality, and biometric identification.

( Image credit: 3DDFA_V2 )

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

9 leaderboard tables shown for this task, 9 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.

DatasetBest model (first row in archive order)PaperCodeSyntologyCompare
REALY (24 rows) HiFace-f HiFace: High-Fidelity 3D Face Reconstruction by Learning Static... — — Compare
REALY (side-view) (19 rows) HiFace-f HiFace: High-Fidelity 3D Face Reconstruction by Learning Static... — — Compare
NoW Benchmark (17 rows) DenseLandmarks (Multi-view) 3D face reconstruction with dense landmarks — — Compare
Florence (16 rows) PRN Joint 3D Face Reconstruction and Dense Alignment with Position Map... code Syntology ran 0 of 10 samples · 10 unverified Compare
AFLW2000-3D (8 rows) SADRNet SADRNet: Self-Aligned Dual Face Regression Networks for Robust 3D... code Syntology ran 0 of 8 samples · 8 unverified Compare
Stirling-HQ (FG2018 3D face reconstruction challenge) (4 rows) DECA Learning an Animatable Detailed 3D Face Model from In-The-Wild Images code — Compare
Stirling-LQ (FG2018 3D face reconstruction challenge) (4 rows) DECA Learning an Animatable Detailed 3D Face Model from In-The-Wild Images code — Compare
13.8 (1 row) Djehutynakht Facial Key Points Detection using Deep Convolutional Neural... code — Compare
!(()&&!|*|*| (1 row) po PointNet: Deep Learning on Point Sets for 3D Classification and... code Syntology ran 89 of 164 samples · 75 unverified 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

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

Subtasks archive 2025-07-28

1 subtask in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

30 shown of 83 papers with code (211 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 9 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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