Papers › MotionAGFormer: Enhancing 3D Human Pose Estimation with a Transformer-GCNFormer Network

MotionAGFormer: Enhancing 3D Human Pose Estimation with a Transformer-GCNFormer Network

25 Oct 2023arXiv:2310.16288archive 2025-07-28

Soroush Mehraban, Vida Adeli, Babak Taati

Recent transformer-based approaches have demonstrated excellent performance in 3D human pose estimation. However, they have a holistic view and by encoding global relationships between all the joints, they do not capture the local dependencies precisely. In this paper, we present a novel Attention-GCNFormer (AGFormer) block that divides the number of channels by using two parallel transformer and GCNFormer streams. Our proposed GCNFormer module exploits the local relationship between adjacent joints, outputting a new representation that is complementary to the transformer output. By fusing these two representation in an adaptive way, AGFormer exhibits the ability to better learn the underlying 3D structure. By stacking multiple AGFormer blocks, we propose MotionAGFormer in four different variants, which can be chosen based on the speed-accuracy trade-off. We evaluate our model on two popular benchmark datasets: Human3.6M and MPI-INF-3DHP. MotionAGFormer-B achieves state-of-the-art results, with P1 errors of 38.4mm and 16.2mm, respectively. Remarkably, it uses a quarter of the parameters and is three times more computationally efficient than the previous leading model on Human3.6M dataset. Code and models are available at https://github.com/TaatiTeam/MotionAGFormer.

PaperPDFCodeCode Syntology ran

In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.

For agents, Syntology's MCP tool lists every function and class Syntology harvested from this paper and whether it ran (how to connect): get_harvested_code_for_paper(arxiv_id="2310.16288")

Code

Syntology Ran 14 of 16 code samples harvested from 1 repository linked to this paper; 2 have no recorded run. Of those that ran: 1 ran · our draft was wrong; 13 ran with no contract checked.

By repository: official repository: 16 samples from 1 repository, 14 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.

taatiteam/motionagformer officialmentioned in papermentioned on GitHubpytorchApache-2.0 report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

16 samples harvested; 14 ran; 0 honoured the contract we drafted; 2 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.

1ran · our draft was wrong
13ran
2unverified

Licence: 0 of the 16 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.

Harvested from taatiteam/motionagformer. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.

Each sample ends with its code_sha256, Syntology's identity for that exact code. An agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

Repository labels, per sample. official repository: The archive marks this repository official for the paper. named in the paper: The archive records that the paper mentions this repository; it is not marked official. community (archive-listed): In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper. found in paper text by Syntology: Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted. community: Not in the archive's code links for this paper; a community repository Syntology harvested. Samples from a repository marked official are listed first. Licence labels name the repository's licence as recorded at harvest. “Pointer only” means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence label for the reason. File links open the file on GitHub at the default branch, which may have changed since the harvest.

acc_error taatiteam/motionagformer/loss/pose3d.py official repository ran Apache-2.0 (permissive) · adcee5fd9395fccd · report
coco_h36m taatiteam/motionagformer/demo/lib/preprocess.py official repository ran Apache-2.0 (permissive) · f3a0f7cf8690505f · report
edge2mat taatiteam/motionagformer/model/modules/ctrgc.py official repository ran · our draft was wrong Apache-2.0 (permissive) · 5f5ae82020ac7ec2 · report
get_spatial_graph taatiteam/motionagformer/model/modules/ctrgc.py official repository ran Apache-2.0 (permissive) · 7bf753e1974cd49d · report
h36m_coco_format taatiteam/motionagformer/demo/lib/preprocess.py official repository ran Apache-2.0 (permissive) · 85be84dc21eb6ce3 · report
jpe taatiteam/motionagformer/loss/pose3d.py official repository ran Apache-2.0 (permissive) · ef4f2e439dde1bc0 · report
mpjpe taatiteam/motionagformer/loss/pose3d.py official repository ran Apache-2.0 (permissive) · 08fdd93d7a20142f · report
normalize_digraph taatiteam/motionagformer/model/modules/ctrgc.py official repository ran fingerprinted Apache-2.0 (permissive) · abe9afd483b702d8 · report
normalize_screen_coordinates taatiteam/motionagformer/demo/lib/utils.py official repository ran Apache-2.0 (permissive) · 1fddccf0fa7a9893 · report
qrot taatiteam/motionagformer/demo/lib/utils.py official repository ran Apache-2.0 (permissive) · 34067eef72357f72 · report
resample taatiteam/motionagformer/demo/vis.py official repository ran fingerprinted Apache-2.0 (permissive) · a7047a1db3eec59b · report
revise_kpts taatiteam/motionagformer/demo/lib/preprocess.py official repository ran Apache-2.0 (permissive) · b9f70678ddc9add2 · report
show2Dpose taatiteam/motionagformer/demo/vis.py official repository ran Apache-2.0 (permissive) · 091700eabe2e929a · report
turn_into_clips taatiteam/motionagformer/demo/vis.py official repository ran Apache-2.0 (permissive) · c084a2b9ed79fa5a · report
input_augmentation taatiteam/motionagformer/train_3dhp.py official repository unverified Apache-2.0 (permissive) · 4ea604a4a73fce53 · report
wrap taatiteam/motionagformer/demo/lib/utils.py official repository unverified Apache-2.0 (permissive) · 162a4d479c76b84e · report

Tasks

3D Human Pose EstimationClassificationMonocular 3D Human Pose EstimationPose Estimation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
3D Human Pose Estimation MPI-INF-3DHP MotionAGFormer-L (T=81) AUC 85.3 #2 of 108 Archive leaderboard report
3D Human Pose Estimation MPI-INF-3DHP MotionAGFormer-L (T=81) MPJPE 16.2 #2 of 108 Archive leaderboard report
3D Human Pose Estimation MPI-INF-3DHP MotionAGFormer-L (T=81) PCK 98.2 #2 of 108 Archive leaderboard report
3D Human Pose Estimation MPI-INF-3DHP MotionAGFormer-S (T=81) AUC 84.5 #4 of 108 Archive leaderboard report
3D Human Pose Estimation MPI-INF-3DHP MotionAGFormer-S (T=81) MPJPE 17.1 #4 of 108 Archive leaderboard report
3D Human Pose Estimation MPI-INF-3DHP MotionAGFormer-S (T=81) PCK 98.3 #4 of 108 Archive leaderboard report
3D Human Pose Estimation MPI-INF-3DHP MotionAGFormer-B (T=81) AUC 84.2 #6 of 108 Archive leaderboard report
3D Human Pose Estimation MPI-INF-3DHP MotionAGFormer-B (T=81) MPJPE 18.2 #6 of 108 Archive leaderboard report
3D Human Pose Estimation MPI-INF-3DHP MotionAGFormer-B (T=81) PCK 98.3 #6 of 108 Archive leaderboard report
3D Human Pose Estimation MPI-INF-3DHP MotionAGFormer-XS (T=27) AUC 83.5 #7 of 108 Archive leaderboard report
3D Human Pose Estimation MPI-INF-3DHP MotionAGFormer-XS (T=27) MPJPE 19.2 #7 of 108 Archive leaderboard report
3D Human Pose Estimation MPI-INF-3DHP MotionAGFormer-XS (T=27) PCK 98.2 #7 of 108 Archive leaderboard report
Classification Full-body Parkinson’s disease dataset MotionAGFormer F1-score (weighted) 0.42 #6 of 7 Archive leaderboard report
Monocular 3D Human Pose Estimation Human3.6M MotionAGFormer-L 2D detector SH #2 of 52 Archive leaderboard report
Monocular 3D Human Pose Estimation Human3.6M MotionAGFormer-L Average MPJPE (mm) 38.4 #2 of 52 Archive leaderboard report
Monocular 3D Human Pose Estimation Human3.6M MotionAGFormer-L Frames Needed 243 #2 of 52 Archive leaderboard report
Monocular 3D Human Pose Estimation Human3.6M MotionAGFormer-L Need Ground Truth 2D Pose No #2 of 52 Archive leaderboard report
Monocular 3D Human Pose Estimation Human3.6M MotionAGFormer-L Use Video Sequence Yes #2 of 52 Archive leaderboard report
Monocular 3D Human Pose Estimation Human3.6M MotionAGFormer-B 2D detector SH #3 of 52 Archive leaderboard report
Monocular 3D Human Pose Estimation Human3.6M MotionAGFormer-B Average MPJPE (mm) 38.4 #3 of 52 Archive leaderboard report
Monocular 3D Human Pose Estimation Human3.6M MotionAGFormer-B Frames Needed 243 #3 of 52 Archive leaderboard report
Monocular 3D Human Pose Estimation Human3.6M MotionAGFormer-B Need Ground Truth 2D Pose No #3 of 52 Archive leaderboard report
Monocular 3D Human Pose Estimation Human3.6M MotionAGFormer-B Use Video Sequence Yes #3 of 52 Archive leaderboard report
Monocular 3D Human Pose Estimation Human3.6M MotionAGFormer-S 2D detector SH #13 of 52 Archive leaderboard report
Monocular 3D Human Pose Estimation Human3.6M MotionAGFormer-S Average MPJPE (mm) 42.5 #13 of 52 Archive leaderboard report
Monocular 3D Human Pose Estimation Human3.6M MotionAGFormer-S Frames Needed 81 #13 of 52 Archive leaderboard report
Monocular 3D Human Pose Estimation Human3.6M MotionAGFormer-S Need Ground Truth 2D Pose No #13 of 52 Archive leaderboard report
Monocular 3D Human Pose Estimation Human3.6M MotionAGFormer-S Use Video Sequence Yes #13 of 52 Archive leaderboard report
Monocular 3D Human Pose Estimation Human3.6M MotionAGFormer-XS 2D detector SH #17 of 52 Archive leaderboard report
Monocular 3D Human Pose Estimation Human3.6M MotionAGFormer-XS Average MPJPE (mm) 45.1 #17 of 52 Archive leaderboard report
Monocular 3D Human Pose Estimation Human3.6M MotionAGFormer-XS Frames Needed 27 #17 of 52 Archive leaderboard report
Monocular 3D Human Pose Estimation Human3.6M MotionAGFormer-XS Need Ground Truth 2D Pose No #17 of 52 Archive leaderboard report
Monocular 3D Human Pose Estimation Human3.6M MotionAGFormer-XS Use Video Sequence Yes #17 of 52 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.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections