3D Human Pose Estimation On Humaneva I
評価指標
Mean Reconstruction Error (mm)
評価結果
このベンチマークにおける各モデルのパフォーマンス結果
比較表
| モデル名 | Mean Reconstruction Error (mm) |
|---|---|
| 3d-human-pose-estimation-from-a-single-image | 26.9 |
| neural-body-fitting-unifying-deep-learning | 64 |
| lifting-transformer-for-3d-human-pose | 18.9 |
| gla-gcn-global-local-adaptive-graph | 9.2 |
| occlusion-aware-networks-for-3d-human-pose | 14.3 |
| learning-dynamical-human-joint-affinity-for | 19.5 |
| depth-sweep-regression-forests-for-estimating | 40.3 |
| unite-the-people-closing-the-loop-between-3d | 74.5 |
| refined-temporal-pyramidal-compression-and | 19.1 |
| keep-it-smpl-automatic-estimation-of-3d-human | 79.9 |
| mixste-seq2seq-mixed-spatio-temporal-encoder | 16.1 |
| a-simple-yet-effective-baseline-for-3d-human | 24.6 |
| learning-pose-grammar-to-encode-human-body | 22.9 |
| hemlets-pose-learning-part-centric-heatmap-1 | 15.2 |
| convformer-parameter-reduction-in-transformer | 24.3 |
| exploiting-temporal-information-for-3d-pose | 22 |
| 3d-human-pose-estimation-using-spatio-1 | 13.5 |
| recurrent-3d-pose-sequence-machines | 30.8 |
| enhanced-3d-human-pose-estimation-from-videos | 15.4 |
| deep-multitask-architecture-for-integrated-2d | 33.7 |
| twin-gaussian-processes-for-structured | 39.1 |
| a-joint-model-for-2d-and-3d-pose-estimation | 56.7 |
| gast-net-graph-attention-spatio-temporal | 21.2 |
| monocular-3d-human-pose-estimation-by-1 | 23.9 |
| ordinal-depth-supervision-for-3d-human-pose | 18.3 |
| robust-estimation-of-3d-human-poses-from-a | 71.3 |
| a-dual-source-approach-for-3d-pose-estimation | 38.9 |
| lifting-transformer-for-3d-human-pose | 12.2 |
| 3d-human-pose-estimation-with-spatial-and | 21.6 |
| coarse-to-fine-volumetric-prediction-for | 24.3 |
| learning-pose-grammar-for-monocular-3d-pose | 22.9 |