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SMPL: a skinned multi-person linear model
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
FAUST: Dataset and Evaluation for 3D Mesh Registration
A novel mesh registration technique that combines 3D shape and appearance information to produce high-quality alignments is addressed with a new dataset called FAUST that contains 300 scans of 10 people in a wide range of poses together with an evaluation methodology. Expand
OpenDR: An Approximate Differentiable Renderer
This work describes a publicly available OpenDR framework that makes it easy to express a forward graphics model and then automatically obtain derivatives with respect to the model parameters and to optimize over them and demonstrates the power and simplicity of programming with OpenDR by using it to solve the problem of estimating human body shape from Kinect depth and RGB data. Expand
MoSh: motion and shape capture from sparse markers
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
Coregistration: Simultaneous Alignment and Modeling of Articulated 3D Shape
By minimizing a single objective function, the model reliably obtain high quality registration of noisy, incomplete, laser scans, while simultaneously learning a highly realistic articulated body model that greatly improves robustness to noise and missing data. Expand
Detailed Full-Body Reconstructions of Moving People from Monocular RGB-D Sequences
This work accurately estimates the 3D geometry and appearance of the human body from a monocular RGB-D sequence of a user moving freely in front of the sensor, allowing it to capture faces with a high-resolution deformable head model and body shape with lower-resolution. Expand
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
The informed sampler: A discriminative approach to Bayesian inference in generative computer vision models
The informed sampler, using simple discriminative proposals based on existing computer vision technology, achieves significant improvements of inference, and is found to converge faster than all baseline samplers across diverse problems. Expand
FACSIMILE: Fast and Accurate Scans From an Image in Less Than a Second
FACSIMILE (FAX) is proposed, a method that estimates a detailed body from a single photo, lowering the bar for creating virtual representations of humans and is easy to implement and fast to execute, making it easily deployable. Expand
FlowCap: 2D Human Pose from Optical Flow
The results suggest that optical flow shares invariances with range data that, when complemented with tracking, make it valuable for pose estimation. Expand