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Human Parsing
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Human parsing is the task of segmenting a human image into different fine-grained semantic parts such as head, torso, arms and legs.
<span style="color:grey; opacity: 0.6">( Image credit: Multi-Human-Parsing (MHP) )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
SCHNet: SAM Marries CLIP for Human Parsing
Progressive Limb-Aware Virtual Try-On
TruePose: Human-Parsing-guided Attention Diffusion for Full-ID Preserving Pose Transfer
MC-VTON: Minimal Control Virtual Try-On Diffusion Transformer
Exploring More from Multiple Gait Modalities for Human Identification
Improving analytical color and texture similarity estimation methods for dataset-agnostic person reidentification
It Takes Two: Accurate Gait Recognition in the Wild via Cross-granularity Alignment
Features Reconstruction Disentanglement Cloth-Changing Person Re-Identification
MTMamba: Enhancing Multi-Task Dense Scene Understanding by Mamba-Based Decoders
4D-DRESS: A 4D Dataset of Real-world Human Clothing with Semantic Annotations
Part-Attention Based Model Make Occluded Person Re-Identification Stronger
Data Augmentation in Human-Centric Vision
Spatial Cascaded Clustering and Weighted Memory for Unsupervised Person Re-identification
DROP: Decouple Re-Identification and Human Parsing with Task-specific Features for Occluded Person Re-identification
Explore Human Parsing Modality for Action Recognition
Synthesizing Environment-Specific People in Photographs
Part Representation Learning with Teacher-Student Decoder for Occluded Person Re-identification
360° Volumetric Portrait Avatar
PICTURE: PhotorealistIC virtual Try-on from UnconstRained dEsigns
UniParser: Multi-Human Parsing with Unified Correlation Representation Learning
Exploring the Robustness of Human Parsers Towards Common Corruptions
Parsing is All You Need for Accurate Gait Recognition in the Wild
DM-VTON: Distilled Mobile Real-time Virtual Try-On
CIParsing: Unifying Causality Properties into Multiple Human Parsing
Semantic-Human: Neural Rendering of Humans from Monocular Video with Human Parsing
Exploring Part-Informed Visual-Language Learning for Person Re-Identification
Integrating Human Parsing and Pose Network for Human Action Recognition
milliFlow: Scene Flow Estimation on mmWave Radar Point Cloud for Human Motion Sensing
Single-stage Multi-human Parsing via Point Sets and Center-based Offsets
Semantic Human Parsing via Scalable Semantic Transfer over Multiple Label Domains
Beyond Appearance: a Semantic Controllable Self-Supervised Learning Framework for Human-Centric Visual Tasks
HumanBench: Towards General Human-centric Perception with Projector Assisted Pretraining
UniHCP: A Unified Model for Human-Centric Perceptions
Texture-Based Input Feature Selection for Action Recognition
Deep Learning for Human Parsing: A Survey
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