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SMPL: a skinned multi-person linear model
TLDR
The Skinned Multi-Person Linear model (SMPL) is a skinned vertex-based model that accurately represents a wide variety of body shapes in natural human poses that is compatible with existing graphics pipelines and iscompatible with existing rendering engines. Expand
Learning from Synthetic Humans
TLDR
This work presents SURREAL (Synthetic hUmans foR REAL tasks): a new large-scale dataset with synthetically-generated but realistic images of people rendered from 3D sequences of human motion capture data and shows that CNNs trained on this synthetic dataset allow for accurate human depth estimation and human part segmentation in real RGB images. Expand
AMASS: Archive of Motion Capture As Surface Shapes
TLDR
AMASS is introduced, a large and varied database of human motion that unifies 15 different optical marker-based mocap datasets by representing them within a common framework and parameterization and makes it readily useful for animation, visualization, and generating training data for deep learning. Expand
Dyna: a model of dynamic human shape in motion
TLDR
The Dyna model realistically represents the dynamics of soft tissue for previously unseen subjects and motions and provides tools for animators to modify the deformations and apply them to new stylized characters. Expand
MoSh: motion and shape capture from sparse markers
TLDR
This work illustrates MoSh by recovering body shape, pose, and soft-tissue motion from archival mocap data and using this to produce animations with subtlety and realism and shows how to magnify the 3D deformations of soft tissue to create animations with appealing exaggerations. Expand
SMPL
We present a learned model of human body shape and pose-dependent shape variation that is more accurate than previous models and is compatible with existing graphics pipelines. Our SkinnedExpand
Dyna
To look human, digital full-body avatars need to have soft-tissue deformations like those of real people. We learn a model of soft-tissue deformations from examples using a high-resolution 4D captureExpand
Breathing life into shape
TLDR
A novel non-linear model relating volume and breathing type to 3D shape deformations and pose changes and an intuitive interface with which animators can synthesize 3D human avatars with realistic breathing motions are developed. Expand
MoSh
Marker-based motion capture (mocap) is widely criticized as producing lifeless animations. We argue that important information about body surface motion is present in standard marker sets but is lostExpand
First Impressions of Personality Traits From Body Shapes
TLDR
This study provides the first comprehensive look at the range, diversity, and reliability of personality inferences that people make from body shapes. Expand
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