Pose Estimation is a general problem in Computer Vision where we detect the position and orientation of an object.
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We demonstrate this framework on 3D pose estimation by proposing a differentiable objective that seeks the optimal set of keypoints for recovering the relative pose between two views of an object.
With recent progress in graphics, it has become more tractable to train models on synthetic images, potentially avoiding the need for expensive annotations.
#4 best model for Image-to-Image Translation on Cityscapes Photo-to-Labels
In this work, we establish dense correspondences between RGB image and a surface-based representation of the human body, a task we refer to as dense human pose estimation.
#2 best model for Pose Estimation on DensePose-COCO
Both convolutional and recurrent operations are building blocks that process one local neighborhood at a time.
#2 best model for Instance Segmentation on COCO
In this work, we present a realtime approach to detect the 2D pose of multiple people in an image.
We present an approach to efficiently detect the 2D pose of multiple people in an image.
#4 best model for Multi-Person Pose Estimation on MPII Multi-Person
What will happen if we increase the dataset size by 10x or 100x?
#8 best model for Image Classification on ImageNet
We propose a unified formulation for the problem of 3D human pose estimation from a single raw RGB image that reasons jointly about 2D joint estimation and 3D pose reconstruction to improve both tasks.
#6 best model for 3D Human Pose Estimation on Human3.6M
There has been significant progress on pose estimation and increasing interests on pose tracking in recent years.
#2 best model for Pose Estimation on COCO