• Corpus ID: 203591800

Exploring Pose Priors for Human Pose Estimation with Joint Angle Representations

@article{Raaj2019ExploringPP,
  title={Exploring Pose Priors for Human Pose Estimation with Joint Angle Representations},
  author={Yaadhav Raaj},
  journal={ArXiv},
  year={2019},
  volume={abs/1909.12761}
}
Pose Priors are critical in human pose estimation, since they are able to enforce constraints that prevent estimated poses from tending to physically impossible positions. Human pose generally consists of up to 22 Joint Angles of various segments, and their respective bone lengths, but the way these various segments interact can affect the validity of a pose. Looking at the Knee-Ankle segment alone, we can observe that clearly, the Knee cannot bend forward beyond it's roughly 90 degree point… 

Figures from this paper

References

SHOWING 1-9 OF 9 REFERENCES
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.
End-to-End Recovery of Human Shape and Pose
TLDR
This work introduces an adversary trained to tell whether human body shape and pose parameters are real or not using a large database of 3D human meshes, and produces a richer and more useful mesh representation that is parameterized by shape and 3D joint angles.
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.
VNect: Real-time 3D Human Pose Estimation with a Single RGB Camera
TLDR
This work presents the first real-time method to capture the full global 3D skeletal pose of a human in a stable, temporally consistent manner using a single RGB camera and shows that the approach is more broadly applicable than RGB-D solutions, i.e., it works for outdoor scenes, community videos, and low quality commodity RGB cameras.
Monocular Total Capture: Posing Face, Body, and Hands in the Wild
TLDR
This work presents the first method to capture the 3D total motion of a target person from a monocular view input, and leverages a 3D deformable human model to reconstruct total body pose from the CNN outputs with the aid of the pose and shape prior in the model.
Bio-LSTM: A Biomechanically Inspired Recurrent Neural Network for 3-D Pedestrian Pose and Gait Prediction
TLDR
A biomechanically inspired recurrent neural network that can predict the location and three-dimensional (3-D) articulated body pose of pedestrians in a global coordinate frame, given 3-D poses and locations estimated in prior frames with inaccuracy is proposed.
Human3.6M: Large Scale Datasets and Predictive Methods for 3D Human Sensing in Natural Environments
We introduce a new dataset, Human3.6M, of 3.6 Million accurate 3D Human poses, acquired by recording the performance of 5 female and 6 male subjects, under 4 different viewpoints, for training
PedX: Benchmark Dataset for Metric 3-D Pose Estimation of Pedestrians in Complex Urban Intersections
TLDR
It is shown that the manual 2-D image labels can be replaced by state-of-the-art automated labeling approaches, thereby facilitating automatic generation of large scale datasets.
DeepHuman: 3D Human Reconstruction From a Single Image
TLDR
DeepHuman, an image-guided volume-to-volume translation CNN for 3D human reconstruction from a single RGB image, leverages a dense semantic representation generated from SMPL model as an additional input to reduce the ambiguities associated with the reconstruction of invisible areas.