Human Parsing
56 papers with code • 1 benchmarks • 2 datasets
Human parsing is the task of segmenting a human image into different fine-grained semantic parts such as head, torso, arms and legs.
( Image credit: Multi-Human-Parsing (MHP) )
Benchmarks
These leaderboards are used to track progress in Human Parsing
| Trend | Dataset | Best Model | Paper | Code | Compare |
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PASCAL Context
MHP
Latest papers
UniHCP: A Unified Model for Human-Centric Perceptions
opengvlab/unihcp
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CVPR 2023
When adapted to a specific task, UniHCP achieves new SOTAs on a wide range of human-centric tasks, e. g., 69. 8 mIoU on CIHP for human parsing, 86. 18 mA on PA-100K for attribute prediction, 90. 3 mAP on Market1501 for ReID, and 85. 8 JI on CrowdHuman for pedestrian detection, performing better than specialized models tailored for each task.
Deep Learning Technique for Human Parsing: A Survey and Outlook
Human parsing aims to partition humans in image or video into multiple pixel-level semantic parts.
Human Co-Parsing Guided Alignment for Occluded Person Re-identification
Vill-Lab/2022-TIP-HCGA
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IEEE Transactions on Image Processing 2022
Most supervised methods propose to train an extra human parsing model aside from the ReID model with cross-domain human parts annotation, suffering from expensive annotation cost and domain gap; Unsupervised methods integrate a feature clustering-based human parsing process into the ReID model, but lacking supervision signals brings less satisfactory segmentation results.
IntegratedPIFu: Integrated Pixel Aligned Implicit Function for Single-view Human Reconstruction
We propose IntegratedPIFu, a new pixel aligned implicit model that builds on the foundation set by PIFuHD.
Body Part-Based Representation Learning for Occluded Person Re-Identification
vlsomers/bpbreid
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Firstly, individual body part appearance is not as discriminative as global appearance (two distinct IDs might have the same local appearance), this means standard ReID training objectives using identity labels are not adapted to local feature learning.
Long-Term Person Re-identification with Dramatic Appearance Change: Algorithm and Benchmark
In addition, we propose a network named M2Net, which integrates multi-modality features from the RGB images, contour images and human parsing images.
Identity-Sensitive Knowledge Propagation for Cloth-Changing Person Re-identification
To mitigate the resolution degradation issue and mine identity-sensitive cues from human faces, we propose to restore the missing facial details using prior facial knowledge, which is then propagated to a smaller network.
Text2Human: Text-Driven Controllable Human Image Generation
In this work, we present a text-driven controllable framework, Text2Human, for a high-quality and diverse human generation.
Versatile Multi-Modal Pre-Training for Human-Centric Perception
To tackle the challenges, we design the novel Dense Intra-sample Contrastive Learning and Sparse Structure-aware Contrastive Learning targets by hierarchically learning a modal-invariant latent space featured with continuous and ordinal feature distribution and structure-aware semantic consistency.
InvPT: Inverted Pyramid Multi-task Transformer for Dense Scene Understanding
Multi-task dense scene understanding is a thriving research domain that requires simultaneous perception and reasoning on a series of correlated tasks with pixel-wise prediction.
