Specifically, we take both the historical motion sequences and coarse prediction as input of our cascaded refinement network to predict refined human motion and strengthen the refinement network with adversarial error augmentation.
Human pose estimation is the task of localizing body keypoints from still images.
In this work, we propose a novel framework named Region-Aware Network (RANet), which learns the ability of anti-confusing in case of heavy occlusion, nearby person and symmetric appearance, for human pose estimation.
Estimation of the covariance matrix for high-dimensional multivariate datasets is a challenging and important problem in modern statistics.
Statistics Theory Methodology Statistics Theory
We present a novel 3D face reconstruction technique that leverages sparse photometric stereo (PS) and latest advances on face registration/modeling from a single image.
Depth from defocus (DfD) and stereo matching are two most studied passive depth sensing schemes.