human-part
Papers with tag human-part
2022
- SUPR: A Sparse Unified Part-Based Human RepresentationAhmed A. A. Osman, Timo Bolkart, Dimitrios Tzionas, and Michael J. BlackIn 2022
Statistical 3D shape models of the head, hands, and fullbody are widely usedin computer vision and graphics. Despite their wide use, we show that existingmodels of the head and hands fail to capture the full range of motion for theseparts. Moreover, existing work largely ignores the feet, which are crucial formodeling human movement and have applications in biomechanics, animation, andthe footwear industry. The problem is that previous body part models aretrained using 3D scans that are isolated to the individual parts. Such datadoes not capture the full range of motion for such parts, e.g. the motion ofhead relative to the neck. Our observation is that full-body scans provideimportant information about the motion of the body parts. Consequently, wepropose a new learning scheme that jointly trains a full-body model andspecific part models using a federated dataset of full-body and body-partscans. Specifically, we train an expressive human body model called SUPR(Sparse Unified Part-Based Human Representation), where each joint strictlyinfluences a sparse set of model vertices. The factorized representationenables separating SUPR into an entire suite of body part models. Note that thefeet have received little attention and existing 3D body models have highlyunder-actuated feet. Using novel 4D scans of feet, we train a model with anextended kinematic tree that captures the range of motion of the toes.Additionally, feet deform due to ground contact. To model this, we include anovel non-linear deformation function that predicts foot deformationconditioned on the foot pose, shape, and ground contact. We train SUPR on anunprecedented number of scans: 1.2 million body, head, hand and foot scans. Wequantitatively compare SUPR and the separated body parts and find that oursuite of models generalizes better than existing models. SUPR is available athttp://supr.is.tue.mpg.de
基于part的人体模型
human human-part human-representation@inproceedings{SUPR, title = {SUPR: A Sparse Unified Part-Based Human Representation}, author = {Osman, Ahmed A. A. and Bolkart, Timo and Tzionas, Dimitrios and Black, Michael J.}, year = {2022}, tags = {human, human-part, human-representation}, }
- SUPR: A Sparse Unified Part-Based Human RepresentationAhmed A. A. Osman, Timo Bolkart, Dimitrios Tzionas, and Michael J. BlackIn 2022
Statistical 3D shape models of the head, hands, and fullbody are widely usedin computer vision and graphics. Despite their wide use, we show that existingmodels of the head and hands fail to capture the full range of motion for theseparts. Moreover, existing work largely ignores the feet, which are crucial formodeling human movement and have applications in biomechanics, animation, andthe footwear industry. The problem is that previous body part models aretrained using 3D scans that are isolated to the individual parts. Such datadoes not capture the full range of motion for such parts, e.g. the motion ofhead relative to the neck. Our observation is that full-body scans provideimportant information about the motion of the body parts. Consequently, wepropose a new learning scheme that jointly trains a full-body model andspecific part models using a federated dataset of full-body and body-partscans. Specifically, we train an expressive human body model called SUPR(Sparse Unified Part-Based Human Representation), where each joint strictlyinfluences a sparse set of model vertices. The factorized representationenables separating SUPR into an entire suite of body part models. Note that thefeet have received little attention and existing 3D body models have highlyunder-actuated feet. Using novel 4D scans of feet, we train a model with anextended kinematic tree that captures the range of motion of the toes.Additionally, feet deform due to ground contact. To model this, we include anovel non-linear deformation function that predicts foot deformationconditioned on the foot pose, shape, and ground contact. We train SUPR on anunprecedented number of scans: 1.2 million body, head, hand and foot scans. Wequantitatively compare SUPR and the separated body parts and find that oursuite of models generalizes better than existing models. SUPR is available athttp://supr.is.tue.mpg.de
基于part的人体模型
human human-part human-representation@inproceedings{SUPR, title = {SUPR: A Sparse Unified Part-Based Human Representation}, author = {Osman, Ahmed A. A. and Bolkart, Timo and Tzionas, Dimitrios and Black, Michael J.}, year = {2022}, tags = {human, human-part, human-representation}, }