Conceitos essenciais
This survey paper explores the significant progress made in accurately predicting human body shape, generating stylish clothing options, detecting landmarks on human bodies, and recognizing clothing attributes using machine learning techniques.
Resumo
This comprehensive survey delves into methodologies and approaches that have advanced human shape and clothing estimation. It discusses the challenges faced, innovations made, and the potential for future developments in this field.
Human shape and clothing estimation have applications in various domains such as online shopping, fashion retail, augmented reality (AR), virtual reality (VR), gaming, animation, visual effects, and more. The accurate estimation of human shape and clothing is crucial for creating realistic characters and enhancing visual quality.
The task of human shape and clothing estimation faces challenges such as variations in body shapes, sizes, poses, diversity of clothing styles, textures, patterns, occlusions caused by overlapping layers or objects. Obtaining labeled training data with precise annotations is also a challenge for building accurate models.
Prior works have contributed to understanding clothing analysis techniques like modeling, retrieval recommendations. Recent advancements focus on methods like Human Mesh Recovery (HMR), Skinned Multi-Person Linear Model (SMPL), PressureNet for tackling complex poses.
Intelligent fashion methods aim to solve tasks using machine learning techniques like landmark detection for predicting key points on clothing items or fashion parsing for segmentation masks. These methods help analyze fashion trends based on attributes like color, pattern, texture.
Recent models use deep learning approaches like FashionNet for attribute recognition based on CNNs or DETR-based models that jointly perform segmentation and attribute recognition. Attention mechanisms play a crucial role in improving feature extraction for accurate predictions.
Future research directions include overcoming dataset biases, scalability issues, real-time performance improvements to enhance virtual experiences and personalized fashion solutions through innovative technologies.
Estatísticas
Accurate modeling of body shapes contributes to visually appealing experiences.
Variations in body shapes pose challenges for accurate estimations.
Challenges include diverse clothing styles affecting estimations.
Occlusions from overlapping layers hinder accurate estimations.
Prior works contribute to understanding clothing analysis techniques.
Recent advancements focus on methods like HMR and SMPL.
Intelligent fashion methods utilize landmark detection for key points prediction.
Deep learning approaches like FashionNet improve attribute recognition accuracy.
Attention mechanisms play a crucial role in feature extraction for accurate predictions.
Citações
"Accurate modeling of body shapes contributes to visually appealing experiences."
"Challenges include diverse clothing styles affecting estimations."
"Occlusions from overlapping layers hinder accurate estimations."