Learnable Triangulation of Human Pose

ICCV 2019 Karim IskakovEgor BurkovVictor LempitskyYury Malkov

We present two novel solutions for multi-view 3D human pose estimation based on new learnable triangulation methods that combine 3D information from multiple 2D views. The first (baseline) solution is a basic differentiable algebraic triangulation with an addition of confidence weights estimated from the input images... (read more)

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Results from the Paper


 Ranked #1 on 3D Human Pose Estimation on Human3.6M (using extra training data)

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TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK USES EXTRA
TRAINING DATA
RESULT BENCHMARK
3D Human Pose Estimation Human3.6M Learnable Triangulation of Human Pose Average MPJPE (mm) 17.7 # 1
Using 2D ground-truth joints No # 1
Multi-View or Monocular Multi-View # 1

Methods used in the Paper


METHOD TYPE
🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet