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 with no code
Part-Attention Based Model Make Occluded Person Re-Identification Stronger
However, occluded person ReID still suffers from background clutter and low-quality local feature representations, which limits model performance.
Data Augmentation in Human-Centric Vision
This survey presents a comprehensive analysis of data augmentation techniques in human-centric vision tasks, a first of its kind in the field.
Spatial Cascaded Clustering and Weighted Memory for Unsupervised Person Re-identification
We introduce the Spatial Cascaded Clustering and Weighted Memory (SCWM) method to address these challenges.
360° Volumetric Portrait Avatar
In contrast to this, we propose a template-based tracking of the torso, head and facial expressions which allows us to cover the appearance of a human subject from all sides.
PICTURE: PhotorealistIC virtual Try-on from UnconstRained dEsigns
Unlike prior arts constrained by specific input types, our method allows flexible specification of style (text or image) and texture (full garment, cropped sections, or texture patches) conditions.
Exploring the Robustness of Human Parsers Towards Common Corruptions
The experimental results show that the proposed method demonstrates good universality which can improve the robustness of the human parsing models and even the semantic segmentation models when facing various image common corruptions.
CIParsing: Unifying Causality Properties into Multiple Human Parsing
Existing methods of multiple human parsing (MHP) apply statistical models to acquire underlying associations between images and labeled body parts.
Semantic-Human: Neural Rendering of Humans from Monocular Video with Human Parsing
In this paper, we present Semantic-Human, a novel method that achieves both photorealistic details and viewpoint-consistent human parsing for the neural rendering of humans.
Exploring Part-Informed Visual-Language Learning for Person Re-Identification
Recently, visual-language learning has shown great potential in enhancing visual-based person re-identification (ReID).
milliFlow: Scene Flow Estimation on mmWave Radar Point Cloud for Human Motion Sensing
In this work, we propose milliFlow, a novel deep learning approach to estimate scene flow as complementary motion information for mmWave point cloud, serving as an intermediate level of features and directly benefiting downstream human motion sensing tasks.
