The trained network can recognize human body parts in depth image. Abstract This paper describes an efficient generation of large-scale dataset of human depth images with body part labels. The size of image datasets has recently been increasingly important as it is shown to be strongly related to...
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To tackle the problem of learning with label noises, this work introduces a purification strategy, called Self-Correction for Human Parsing (SCHP), to progressively promote the reliability of the supervised labels as well as the learned models. 2 Paper Code Hulk...
HAKE-AVA: Human body part state labels in videos from AVA dataset. CLIP-A2V: CLIP-based part states & verb recognizer. HAKE-A2V(CVPR'20): Activity2Vec, a general activity feature extractor based on HAKE data, converting a human (box) to a fixed-size vector, PaSta and action scores. ...
The task of reconstructing detailed 3D human body models from images is interesting but challenging in computer vision due to the high freedom of human bodies. This work proposes a coarse-to-fine method to reconstruct detailed 3D human body from multi-view images combining Voxel Super-Resolution ...
We propose a real-time method for simultaneously refining the reconstructed volume of a human body with loose-fitting clothing and identifying body-parts in it. Time-series volumes, which are acquired by a slow but sophisticated 3D reconstruction algorithm, with body-part labels are obtained ...
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(2022.02.14) We release the human body part state labels based on AVA:HAKE-AVA. (2021.2.7) UpgradedHAKE-Activity2Vecis released! Images/Videos --> human box + ID + skeleton + part states + action + representation.[Description] (2021.1.15) Our extended version ofTINis accepted byTPAMI!
Medical Human Body Muscles Anatomical Simulator Human Manikin with Internal Organ CHJ-A10001 Features:The model is placed on the base plate, consists of a male head, neck, torso, and upper and lower limbs; comparative display of skin, muscles, thoracic cav...
The Human Pose Estimator model detects humans and their poses in a given image. The model first detects the humans in the input image and then identifies the body parts, including nose, neck, eyes, shoulders, elbows, wrists, hips, knees, and ankles. Next, each pair of associated body part...