SUPR: A Sparse Unified Part-Based Human Representation

10/25/2022
by   Ahmed A. A. Osman, et al.
0

Statistical 3D shape models of the head, hands, and fullbody are widely used in computer vision and graphics. Despite their wide use, we show that existing models of the head and hands fail to capture the full range of motion for these parts. Moreover, existing work largely ignores the feet, which are crucial for modeling human movement and have applications in biomechanics, animation, and the footwear industry. The problem is that previous body part models are trained using 3D scans that are isolated to the individual parts. Such data does not capture the full range of motion for such parts, e.g. the motion of head relative to the neck. Our observation is that full-body scans provide important information about the motion of the body parts. Consequently, we propose a new learning scheme that jointly trains a full-body model and specific part models using a federated dataset of full-body and body-part scans. Specifically, we train an expressive human body model called SUPR (Sparse Unified Part-Based Human Representation), where each joint strictly influences a sparse set of model vertices. The factorized representation enables separating SUPR into an entire suite of body part models. Note that the feet have received little attention and existing 3D body models have highly under-actuated feet. Using novel 4D scans of feet, we train a model with an extended kinematic tree that captures the range of motion of the toes. Additionally, feet deform due to ground contact. To model this, we include a novel non-linear deformation function that predicts foot deformation conditioned on the foot pose, shape, and ground contact. We train SUPR on an unprecedented number of scans: 1.2 million body, head, hand and foot scans. We quantitatively compare SUPR and the separated body parts and find that our suite of models generalizes better than existing models. SUPR is available at http://supr.is.tue.mpg.de

READ FULL TEXT

page 2

page 10

page 14

page 22

page 24

page 27

page 28

page 36

research
12/17/2013

Estimation of Human Body Shape and Posture Under Clothing

Estimating the body shape and posture of a dressed human subject in moti...
research
08/19/2020

STAR: Sparse Trained Articulated Human Body Regressor

The SMPL body model is widely used for the estimation, synthesis, and an...
research
06/13/2023

Pose-aware Attention Network for Flexible Motion Retargeting by Body Part

Motion retargeting is a fundamental problem in computer graphics and com...
research
06/21/2023

Modelling human seat contact interaction for vibration comfort

The seat to head vibration transmissibility depends on various character...
research
04/21/2023

BPJDet: Extended Object Representation for Generic Body-Part Joint Detection

Detection of human body and its parts (e.g., head or hands) has been int...
research
03/19/2015

Building Statistical Shape Spaces for 3D Human Modeling

Statistical models of 3D human shape and pose learned from scan database...
research
01/05/2018

Total Capture: A 3D Deformation Model for Tracking Faces, Hands, and Bodies

We present a unified deformation model for the markerless capture of mul...

Please sign up or login with your details

Forgot password? Click here to reset