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
Most implemented papers
Graphonomy: Universal Image Parsing via Graph Reasoning and Transfer
Gaoyiminggithub/Graphonomy-Panoptic
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Prior highly-tuned image parsing models are usually studied in a certain domain with a specific set of semantic labels and can hardly be adapted into other scenarios (e. g., sharing discrepant label granularity) without extensive re-training.
Parser-Free Virtual Try-on via Distilling Appearance Flows
A recent pioneering work employed knowledge distillation to reduce the dependency of human parsing, where the try-on images produced by a parser-based method are used as supervisions to train a "student" network without relying on segmentation, making the student mimic the try-on ability of the parser-based model.
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.
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.
Deep Human Parsing with Active Template Regression
The first CNN network is with max-pooling, and designed to predict the template coefficients for each label mask, while the second CNN network is without max-pooling to preserve sensitivity to label mask position and accurately predict the active shape parameters.
Look into Person: Self-supervised Structure-sensitive Learning and A New Benchmark for Human Parsing
Human parsing has recently attracted a lot of research interests due to its huge application potentials.
Holistic, Instance-Level Human Parsing
We address this problem by segmenting the parts of objects at an instance-level, such that each pixel in the image is assigned a part label, as well as the identity of the object it belongs to.
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
To address the two kinds of inconsistencies, this paper proposes the Macro-Micro Adversarial Net (MMAN).
Instance-level Human Parsing via Part Grouping Network
Engineering-Course/CIHP_PGN
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ECCV 2018
Instance-level human parsing towards real-world human analysis scenarios is still under-explored due to the absence of sufficient data resources and technical difficulty in parsing multiple instances in a single pass.
