Human Part Segmentation

14 papers with code • 6 benchmarks • 9 datasets

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Most implemented papers

Mask R-CNN

tensorflow/models ICCV 2017

Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance.

Cityscapes-Panoptic-Parts and PASCAL-Panoptic-Parts datasets for Scene Understanding

tue-mps/panoptic_parts 16 Apr 2020

In this technical report, we present two novel datasets for image scene understanding.

Cross-Domain Complementary Learning Using Pose for Multi-Person Part Segmentation

kevinlin311tw/CDCL-human-part-segmentation 11 Jul 2019

On the other hand, if part labels are also available in the real-images during training, our method outperforms the supervised state-of-the-art methods by a large margin.

Learning from Synthetic Humans

gulvarol/surreal CVPR 2017

In this work we present SURREAL (Synthetic hUmans foR REAL tasks): a new large-scale dataset with synthetically-generated but realistic images of people rendered from 3D sequences of human motion capture data.

Parsing R-CNN for Instance-Level Human Analysis

soeaver/Parsing-R-CNN CVPR 2019

Models need to distinguish different human instances in the image panel and learn rich features to represent the details of each instance.

Self-Correction for Human Parsing

PeikeLi/Self-Correction-Human-Parsing 22 Oct 2019

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.

Hulk: A Universal Knowledge Translator for Human-Centric Tasks

opengvlab/humanbench 4 Dec 2023

Human-centric perception tasks, e. g., pedestrian detection, skeleton-based action recognition, and pose estimation, have wide industrial applications, such as metaverse and sports analysis.

UberNet: Training a `Universal' Convolutional Neural Network for Low-, Mid-, and High-Level Vision using Diverse Datasets and Limited Memory

EPFL-VILAB/XDEnsembles 7 Sep 2016

In this work we introduce a convolutional neural network (CNN) that jointly handles low-, mid-, and high-level vision tasks in a unified architecture that is trained end-to-end.

Weakly and Semi Supervised Human Body Part Parsing via Pose-Guided Knowledge Transfer


In this paper, we present a novel method to generate synthetic human part segmentation data using easily-obtained human keypoint annotations.

Macro-Micro Adversarial Network for Human Parsing

RoyalVane/MMAN ECCV 2018

To address the two kinds of inconsistencies, this paper proposes the Macro-Micro Adversarial Net (MMAN).