BLSM: A Bone-Level Skinned Model of the Human Mesh. H Wang, RA Guler, I Kokkinos, G Papandreou, and S Zafeiriou.
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Learning to Dress 3D People in Generative Clothing. Q Ma, J Yang, A Ranjan, S Pujades, G Pons-Moll, S Tang, MJ Black.
Date: June 2020. Source: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA. Abstract: Three-dimensional human body models are widely used in the analysis of human pose and motion. Existing models, however, are learned from minimally-clothed 3D scans and thus do not generalize to the complexity of dressed people in common…
AMASS: Archive of Motion Capture as Surface Shapes. N Mahmood , N Ghorbani, NF Troje, G Pons-Moll, MJ Black.
Date: April 2019. Source: Cornell University Library – arXiv.org, Computer Vision and Pattern Recognition. Abstract: Large datasets are the cornerstone of recent advances in computer vision using deep learning. In contrast, existing human motion capture (mocap) datasets are small and the motions limited, hampering progress on learning models of human motion. While there are many…
Three-dimensional motion analysis – an exploratory study. Part 1: Assessment of facial movement. H Popat, S Richmond, R Playle, D Marshall, PL Rosin, D Cosker.
Date: November 2008. Source: Orthodontic Craniofacial Research;11(4):216-223. doi:10.1111/j.1601-343.2008.00433.x. Objective: To objectively quantify facial movement in response to facial expression and spoken word. Design: Experimental study. Setting – Department of Dental Health and Biological Sciences, University Dental Hospital, Cardiff, UK. Experimental variable: Facial movement was assessed in response to a standardized smile expression and the utterance…