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Found 8 papers, 3 papers with code
3D Pose Based Feedback for Physical Exercises
To evaluate our approach, we introduce a dataset with 3 different physical exercises.
Dyadic Human Motion Prediction
To evaluate this, and because no existing motion prediction datasets depict two closely-interacting subjects, we introduce the LindyHop600K dance dataset.
Human Detection and Segmentation via Multi-view Consensus
Self-supervised detection and segmentation of foreground objects aims for accuracy without annotated training data.
Self-supervised Segmentation via Background Inpainting
While supervised object detection and segmentation methods achieve impressive accuracy, they generalize poorly to images whose appearance significantly differs from the data they have been trained on.
Self-supervised Training of Proposal-based Segmentation via Background Prediction
While supervised object detection methods achieve impressive accuracy, they generalize poorly to images whose appearance significantly differs from the data they have been trained on.
Neural Scene Decomposition for Multi-Person Motion Capture
To this end, we introduce a self-supervised approach to learning what we call a neural scene decomposition (NSD) that can be exploited for 3D pose estimation.
Learning Monocular 3D Human Pose Estimation from Multi-view Images
Accurate 3D human pose estimation from single images is possible with sophisticated deep-net architectures that have been trained on very large datasets.
Structured Prediction of 3D Human Pose with Deep Neural Networks
Most recent approaches to monocular 3D pose estimation rely on Deep Learning.
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3D Human Pose Estimation
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