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Models/RepNet

RepNet

Reported on 22 benchmarks across 5 tasks · 3 papers · 6 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Computer Vision19 results

Repetitive Action CountingonCountix
OBO· 2024-11-13
0.7047
SOTA
A Short Note on Evaluating RepNet for Temporal Repetition Counting in Videos arXiv:2411.08878
Repetitive Action CountingonUCFRep
MAE· 2024-11-13
0.2088
SOTA
A Short Note on Evaluating RepNet for Temporal Repetition Counting in Videos arXiv:2411.08878
Repetitive Action CountingonUCFRep
OBO· 2024-11-13
0.7333
SOTA
A Short Note on Evaluating RepNet for Temporal Repetition Counting in Videos arXiv:2411.08878
Repetitive Action CountingonCountix
MAE· 2020-06-27
0.3641
best: 0.276 (ESCounts)
SOTA
Counting Out Time: Class Agnostic Video Repetition Counting in the Wild arXiv:2006.15418
Repetitive Action CountingonCountix
OBO· 2020-06-27
0.3034
best: 0.7047
SOTA
Counting Out Time: Class Agnostic Video Repetition Counting in the Wild arXiv:2006.15418
Repetitive Action CountingonRepCount
OBO· 2020-06-27
0.013
best: 0.563 (ESCounts)
SOTA
Counting Out Time: Class Agnostic Video Repetition Counting in the Wild arXiv:2006.15418
Repetitive Action CountingonCountix
MAE· 2024-11-13
0.3002
best: 0.276 (ESCounts)
A Short Note on Evaluating RepNet for Temporal Repetition Counting in Videos arXiv:2411.08878
Repetitive Action CountingonRepCount
MAE· 2024-11-13
0.3308
best: 0.213 (ESCounts)
A Short Note on Evaluating RepNet for Temporal Repetition Counting in Videos arXiv:2411.08878
Repetitive Action CountingonRepCount
OBO· 2024-11-13
0.5329
best: 0.563 (ESCounts)
A Short Note on Evaluating RepNet for Temporal Repetition Counting in Videos arXiv:2411.08878
3D Human Pose EstimationonHuman3.6M
Average MPJPE (mm)· 2019-02-26
89.9
best: 131.7 (Rhodin et al.)
RepNet: Weakly Supervised Training of an Adversarial Reprojection Network for 3D Human Pose Estimation arXiv:1902.09868
3D Human Pose EstimationonHuman3.6M
Frames Needed· 2019-02-26
1
best: 300 (Sparseness Meets Deepness)
RepNet: Weakly Supervised Training of an Adversarial Reprojection Network for 3D Human Pose Estimation arXiv:1902.09868
3D Human Pose EstimationonHuman3.6M
Average MPJPE (mm)· 2019-02-26
89.9
best: 131.7 (Rhodin et al.)
RepNet: Weakly Supervised Training of an Adversarial Reprojection Network for 3D Human Pose Estimation arXiv:1902.09868
3D Human Pose EstimationonHuman3.6M
Number of Frames Per View· 2019-02-26
1
best: 243 (VideoPose3D (T=243))
RepNet: Weakly Supervised Training of an Adversarial Reprojection Network for 3D Human Pose Estimation arXiv:1902.09868
3D Human Pose EstimationonHuman3.6M
Number of Views· 2019-02-26
1
best: 2 (Kocabas et al.)
RepNet: Weakly Supervised Training of an Adversarial Reprojection Network for 3D Human Pose Estimation arXiv:1902.09868
Pose EstimationonHuman3.6M
Average MPJPE (mm)· 2019-02-26
89.9
best: 131.7 (Rhodin et al.)
RepNet: Weakly Supervised Training of an Adversarial Reprojection Network for 3D Human Pose Estimation arXiv:1902.09868
Pose EstimationonHuman3.6M
Frames Needed· 2019-02-26
1
best: 300 (Sparseness Meets Deepness)
RepNet: Weakly Supervised Training of an Adversarial Reprojection Network for 3D Human Pose Estimation arXiv:1902.09868
Pose EstimationonHuman3.6M
Average MPJPE (mm)· 2019-02-26
89.9
best: 131.7 (Rhodin et al.)
RepNet: Weakly Supervised Training of an Adversarial Reprojection Network for 3D Human Pose Estimation arXiv:1902.09868
Pose EstimationonHuman3.6M
Number of Frames Per View· 2019-02-26
1
best: 243 (VideoPose3D (T=243))
RepNet: Weakly Supervised Training of an Adversarial Reprojection Network for 3D Human Pose Estimation arXiv:1902.09868
Pose EstimationonHuman3.6M
Number of Views· 2019-02-26
1
best: 2 (Kocabas et al.)
RepNet: Weakly Supervised Training of an Adversarial Reprojection Network for 3D Human Pose Estimation arXiv:1902.09868

Methodology5 results

3DonHuman3.6M
Average MPJPE (mm)· 2019-02-26
89.9
best: 131.7 (Rhodin et al.)
RepNet: Weakly Supervised Training of an Adversarial Reprojection Network for 3D Human Pose Estimation arXiv:1902.09868
3DonHuman3.6M
Frames Needed· 2019-02-26
1
best: 300 (Sparseness Meets Deepness)
RepNet: Weakly Supervised Training of an Adversarial Reprojection Network for 3D Human Pose Estimation arXiv:1902.09868
3DonHuman3.6M
Average MPJPE (mm)· 2019-02-26
89.9
best: 131.7 (Rhodin et al.)
RepNet: Weakly Supervised Training of an Adversarial Reprojection Network for 3D Human Pose Estimation arXiv:1902.09868
3DonHuman3.6M
Number of Frames Per View· 2019-02-26
1
best: 243 (VideoPose3D (T=243))
RepNet: Weakly Supervised Training of an Adversarial Reprojection Network for 3D Human Pose Estimation arXiv:1902.09868
3DonHuman3.6M
Number of Views· 2019-02-26
1
best: 2 (Kocabas et al.)
RepNet: Weakly Supervised Training of an Adversarial Reprojection Network for 3D Human Pose Estimation arXiv:1902.09868

Audio5 results

1 Image, 2*2 StitchionHuman3.6M
Average MPJPE (mm)· 2019-02-26
89.9
best: 131.7 (Rhodin et al.)
RepNet: Weakly Supervised Training of an Adversarial Reprojection Network for 3D Human Pose Estimation arXiv:1902.09868
1 Image, 2*2 StitchionHuman3.6M
Frames Needed· 2019-02-26
1
best: 300 (Sparseness Meets Deepness)
RepNet: Weakly Supervised Training of an Adversarial Reprojection Network for 3D Human Pose Estimation arXiv:1902.09868
1 Image, 2*2 StitchionHuman3.6M
Average MPJPE (mm)· 2019-02-26
89.9
best: 131.7 (Rhodin et al.)
RepNet: Weakly Supervised Training of an Adversarial Reprojection Network for 3D Human Pose Estimation arXiv:1902.09868
1 Image, 2*2 StitchionHuman3.6M
Number of Frames Per View· 2019-02-26
1
best: 243 (VideoPose3D (T=243))
RepNet: Weakly Supervised Training of an Adversarial Reprojection Network for 3D Human Pose Estimation arXiv:1902.09868
1 Image, 2*2 StitchionHuman3.6M
Number of Views· 2019-02-26
1
best: 2 (Kocabas et al.)
RepNet: Weakly Supervised Training of an Adversarial Reprojection Network for 3D Human Pose Estimation arXiv:1902.09868