Pose Estimation is a general problem in Computer Vision where we detect the position and orientation of an object.
( Image credit: Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose )
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Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance.
3D INSTANCE SEGMENTATION HUMAN PART SEGMENTATION KEYPOINT DETECTION MULTI-HUMAN PARSING MULTI-PERSON POSE ESTIMATION MULTI-TISSUE NUCLEUS SEGMENTATION NUCLEAR SEGMENTATION PANOPTIC SEGMENTATION REAL-TIME OBJECT DETECTION
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.
#7 best model for Keypoint Detection on COCO (Validation AP metric)
OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation
In this paper, we propose a novel regional multi-person pose estimation (RMPE) framework to facilitate pose estimation in the presence of inaccurate human bounding boxes.
High-resolution representations are essential for position-sensitive vision problems, such as human pose estimation, semantic segmentation, and object detection.
#7 best model for Semantic Segmentation on PASCAL Context