AdaFuse: Adaptive Multiview Fusion for Accurate Human Pose Estimation in the Wild

Abstract

Occlusion is probably the biggest challenge for human pose estimation in the wild. Typical solutions often rely on intrusive sensors such as IMUs to detect occluded joints. To make the task truly unconstrained, we present AdaFuse, an adaptive multiview fusion method, which can enhance the features in occluded views by leveraging those in visible views. The core of AdaFuse is to determine the point-point correspondence between two views which we solve effectively by exploring the sparsity of the heatmap representation. We also learn an adaptive fusion weight for each camera view to reflect its feature quality in order to reduce the chance that good features are undesirably corrupted by ``bad'' views. The fusion model is trained end-to-end with the pose estimation network, and can be directly applied to new camera configurations without additional adaptation. We extensively evaluate the approach on three public datasets including Human3.6M, Total Capture and CMU Panoptic. It outperforms the state-of-the-arts on all of them. We also create a large scale synthetic dataset Occlusion-Person, which allows us to perform numerical evaluation on the occluded joints, as it provides occlusion labels for every joint in the images. The dataset and code are released at https://github.com/zhezh/adafuse-3d-human-pose.

arXiv commentAccepted by IJCV

References in corpus (6)

• On Calibration of Modern Neural Networks
• Learning from Synthetic Humans
• Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
• On Fairness and Calibration
• MoCap-guided Data Augmentation for 3D Pose Estimation in the Wild
• MetaFuse: A Pre-trained Fusion Model for Human Pose Estimation

Cited by in corpus (6)

• Vision-Based Human Pose Estimation via Deep Learning: A Survey
• Adaptive Multi-view and Temporal Fusing Transformer for 3D Human Pose Estimation
• Deep Learning-Based Human Pose Estimation: A Survey
• TransFusion: Cross-view Fusion with Transformer for 3D Human Pose Estimation
• Deep learning for 3D human pose estimation and mesh recovery: A survey
• Ego3DPose: Capturing 3D Cues from Binocular Egocentric Views