Deep representation learning for human motion prediction and classification

Generative models of 3D human motion are often restricted to a small number of activities and can therefore not generalize well to novel movements or applications. In this work we propose a deep learning framework for human motion capture data that learns a generic representation from a large corpus of motion capture data and generalizes well to new, unseen, motions. Using an encoding-decoding network that learns to predict future 3D poses from the most recent past, we extract a feature representation of human motion. Most work on deep learning for sequence prediction focuses on video and speech. Since skeletal data has a different structure, we present and evaluate different network architectures that make different assumptions about time dependencies and limb correlations. To quantify the learned features, we use the output of different layers for action classification and visualize the receptive fields of the network units. Our method outperforms the recent state of the art in skeletal motion prediction even though these use action specific training data. Our results show that deep feedforward networks, trained from a generic mocap database, can successfully be used for feature extraction from human motion data and that this representation can be used as a foundation for classification and prediction.Comment: This paper is published at the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 201

"Sticky Hands": learning and generalization for cooperative physical interactions with a humanoid robot

"Sticky Hands" is a physical game for two people involving gentle contact with the hands. The aim is to develop relaxed and elegant motion together, achieve physical sensitivity-improving reactions, and experience an interaction at an intimate yet comfortable level for spiritual development and physical relaxation. We developed a control system for a humanoid robot allowing it to play Sticky Hands with a human partner. We present a real implementation including a physical system, robot control, and a motion learning algorithm based on a generalizable intelligent system capable itself of generalizing observed trajectories' translation, orientation, scale and velocity to new data, operating with scalable speed and storage efficiency bounds, and coping with contact trajectories that evolve over time. Our robot control is capable of physical cooperation in a force domain, using minimal sensor input. We analyze robot-human interaction and relate characteristics of our motion learning algorithm with recorded motion profiles. We discuss our results in the context of realistic motion generation and present a theoretical discussion of stylistic and affective motion generation based on, and motivating cross-disciplinary research in computer graphics, human motion production and motion perception

Human Motion Capture Data Tailored Transform Coding

Human motion capture (mocap) is a widely used technique for digitalizing human movements. With growing usage, compressing mocap data has received increasing attention, since compact data size enables efficient storage and transmission. Our analysis shows that mocap data have some unique characteristics that distinguish themselves from images and videos. Therefore, directly borrowing image or video compression techniques, such as discrete cosine transform, does not work well. In this paper, we propose a novel mocap-tailored transform coding algorithm that takes advantage of these features. Our algorithm segments the input mocap sequences into clips, which are represented in 2D matrices. Then it computes a set of data-dependent orthogonal bases to transform the matrices to frequency domain, in which the transform coefficients have significantly less dependency. Finally, the compression is obtained by entropy coding of the quantized coefficients and the bases. Our method has low computational cost and can be easily extended to compress mocap databases. It also requires neither training nor complicated parameter setting. Experimental results demonstrate that the proposed scheme significantly outperforms state-of-the-art algorithms in terms of compression performance and speed

Volumetric microvascular imaging of human retina using optical coherence tomography with a novel motion contrast technique

Phase variance-based motion contrast imaging is demonstrated using a spectral domain optical coherence tomography system for the in vivo human retina. This contrast technique spatially identifies locations of motion within the retina primarily associated with vasculature. Histogram-based noise analysis of the motion contrast images was used to reduce the motion noise created by transverse eye motion. En face summation images created from the 3D motion contrast data are presented with segmentation of selected retinal layers to provide non-invasive vascular visualization comparable to currently used invasive angiographic imaging. This motion contrast technique has demonstrated the ability to visualize resolution-limited vasculature independent of vessel orientation and flow velocity