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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
A Simple Yet Effective Baseline for 3d Human Pose Estimation
TLDR
The results indicate that a large portion of the error of modern deep 3d pose estimation systems stems from their visual analysis, and suggests directions to further advance the state of the art in 3d human pose estimation. Expand
Keep It SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image
TLDR
The first method to automatically estimate the 3D pose of the human body as well as its 3D shape from a single unconstrained image is described, showing superior pose accuracy with respect to the state of the art. Expand
On Human Motion Prediction Using Recurrent Neural Networks
TLDR
It is shown that, surprisingly, state of the art performance can be achieved by a simple baseline that does not attempt to model motion at all, and a simple and scalable RNN architecture is proposed that obtains state-of-the-art performance on human motion prediction. Expand
FAUST: Dataset and Evaluation for 3D Mesh Registration
TLDR
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
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
Unite the People: Closing the Loop Between 3D and 2D Human Representations
TLDR
This work proposes a hybrid approach to 3D body model fits for multiple human pose datasets with an extended version of the recently introduced SMPLify method, and shows that UP-3D can be enhanced with these improved fits to grow in quantity and quality, which makes the system deployable on large scale. Expand
Dynamic FAUST: Registering Human Bodies in Motion
TLDR
This work proposes a new mesh registration method that uses both 3D geometry and texture information to register all scans in a sequence to a common reference topology, and shows how using geometry alone results in significant errors in alignment when the motions are fast and non-rigid. Expand
Learning a model of facial shape and expression from 4D scans
TLDR
Faces Learned with an Articulated Model and Expressions is low-dimensional but more expressive than the FaceWarehouse model and the Basel Face Model and is compared to these models by fitting them to static 3D scans and 4D sequences using the same optimization method. 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
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