3D Human Pose Estimation is the task of estimating the pose of a human from a picture or set of video frames.
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We present a novel data-driven regularizer for weakly-supervised learning of 3D human pose estimation that eliminates the drift problem that affects existing approaches.
We propose a new 3D holistic++ scene understanding problem, which jointly tackles two tasks from a single-view image: (i) holistic scene parsing and reconstruction---3D estimations of object bounding boxes, camera pose, and room layout, and (ii) 3D human pose estimation.
Specifically, a skeletal representation is proposed by transforming the joint coordinate sequence into an image sequence, which can model the different correlations of different joints.
SOTA for Pose Prediction on Gaming 3D (G3D)
We propose a scalable neural network framework to reconstruct the 3D mesh of a human body from multi-view images, in the subspace of the SMPL model.
#5 best model for 3D Human Pose Estimation on Human3.6M
To motivate this, we show that current 3D human pose estimation methods produce results that are not consistent with the 3D scene.
Estimating 3D human pose from monocular images demands large amounts of 3D pose and in-the-wild 2D pose annotated datasets which are costly and require sophisticated systems to acquire.
We present an approach to accurately estimate high fidelity markerless 3D pose and volumetric reconstruction of human performance using only a small set of camera views ($\sim 2$).
#8 best model for 3D Human Pose Estimation on Human3.6M
In this paper, we tackle the problem of 3D human shape estimation from single RGB images.
In the second, we deploy the Efficient Neural Architecture Search (ENAS) algorithm to find an optimal network architecture that is used for modeling the spatio-temporal evolution of the estimated 3D poses via an image-based intermediate representation and performing action recognition.
#3 best model for 3D Human Pose Estimation on Human3.6M
Simulation is an anonymous, low-bias source of data where annotation can often be done automatically; however, for some tasks, current models trained on synthetic data generalize poorly to real data.