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3D Human Pose Estimation On Total Capture

Métriques

Average MPJPE (mm)

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle
Average MPJPE (mm)
Paper TitleRepository
IMUPVH70Total capture: 3D human pose estimation fusing video and inertial sensors-
MTF-Transformer (M=0.4, T=7)29.2Adaptive Multi-view and Temporal Fusing Transformer for 3D Human Pose Estimation-
AdaFuse19.2AdaFuse: Adaptive Multiview Fusion for Accurate Human Pose Estimation in the Wild
AutoEnc35Deep Autoencoder for Combined Human Pose Estimation and body Model Upscaling-
LWCDR27.5Lightweight Multi-View 3D Pose Estimation through Camera-Disentangled Representation-
PVH107Total capture: 3D human pose estimation fusing video and inertial sensors-
Tri-CPM99Convolutional Pose Machines
DeepFuse-IMU28.9DeepFuse: An IMU-Aware Network for Real-Time 3D Human Pose Estimation from Multi-View Image-
AdaDeepFuse22.5FusePose: IMU-Vision Sensor Fusion in Kinematic Space for Parametric Human Pose Estimation-
ROS node wrapping1123D Human Pose Estimation in RGBD Images for Robotic Task Learning
Fusion-RPSM29.0Cross View Fusion for 3D Human Pose Estimation
Single-RPSM41.0Cross View Fusion for 3D Human Pose Estimation
GeoFuse24.6Fusing Wearable IMUs with Multi-View Images for Human Pose Estimation: A Geometric Approach
DeepFuse-Vision Only32.7DeepFuse: An IMU-Aware Network for Real-Time 3D Human Pose Estimation from Multi-View Image-
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3D Human Pose Estimation On Total Capture | SOTA | HyperAI