16 citations · 49 across the 11 of their papers we have counts for
13 papers
MOFA-VTON: More Fashion Possibilities with Fine-Grained Adaptations in Virtual Try-On
Xiaoyu Han, Chenyang Wang, Jing Wang +3
Virtual try-on aims to fit an in-shop clothing image onto a specific human body. An optimal virtual try-on method should provide diverse and flexible dressing options, accurately r…
Generalizable and Relightable Gaussian Splatting for Human Novel View Synthesis
Yipengjing Sun, Shengping Zhang, Chenyang Wang +3
We propose GRGS, a generalizable and relightable 3D Gaussian framework for high-fidelity human novel view synthesis under diverse lighting conditions. Unlike existing methods that…
Shape-Guided Clothing Warping for Virtual Try-On
Xiaoyu Han, Shunyuan Zheng, Zonglin Li +3
Image-based virtual try-on aims to seamlessly fit in-shop clothing to a person image while maintaining pose consistency. Existing methods commonly employ the thin plate spline (TPS…
NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results
Zheng Chen, Jingkai Wang, Kai Liu +51
This paper provides a review of the NTIRE 2025 challenge on real-world face restoration, highlighting the proposed solutions and the resulting outcomes. The challenge focuses on ge…
Balancing Task-invariant Interaction and Task-specific Adaptation for Unified Image Fusion
Xingyu Hu, Junjun Jiang, Chenyang Wang +3
Unified image fusion aims to integrate complementary information from multi-source images, enhancing image quality through a unified framework applicable to diverse fusion tasks. W…
Progressive Limb-Aware Virtual Try-On
Xiaoyu Han, Shengping Zhang, Qinglin Liu +2
Existing image-based virtual try-on methods directly transfer specific clothing to a human image without utilizing clothing attributes to refine the transferred clothing geometry a…
LLV-FSR: Exploiting Large Language-Vision Prior for Face Super-resolution
Chenyang Wang, Wenjie An, Kui Jiang +2
Existing face super-resolution (FSR) methods have made significant advancements, but they primarily super-resolve face with limited visual information, original pixel-wise space in…
Enhancing Consistency and Mitigating Bias: A Data Replay Approach for Incremental Learning
Chenyang Wang, Junjun Jiang, Xingyu Hu +2
Deep learning systems are prone to catastrophic forgetting when learning from a sequence of tasks, as old data from previous tasks is unavailable when learning a new task. To addre…
Super-Resolving Face Image by Facial Parsing Information
Chenyang Wang, Junjun Jiang, Zhiwei Zhong +2
Face super-resolution is a technology that transforms a low-resolution face image into the corresponding high-resolution one. In this paper, we build a novel parsing map guided fac…
ReSmooth: Detecting and Utilizing OOD Samples when Training with Data Augmentation
Chenyang Wang, Junjun Jiang, Xiong Zhou +1
Data augmentation (DA) is a widely used technique for enhancing the training of deep neural networks. Recent DA techniques which achieve state-of-the-art performance always meet th…
From Less to More: Spectral Splitting and Aggregation Network for Hyperspectral Face Super-Resolution
Junjun Jiang, Chenyang Wang, Xianming Liu +2
High-resolution (HR) hyperspectral face image plays an important role in face related computer vision tasks under uncontrolled conditions, such as low-light environment and spoofin…
Learning with Noisy Labels via Sparse Regularization
Xiong Zhou, Xianming Liu, Chenyang Wang +3
Learning with noisy labels is an important and challenging task for training accurate deep neural networks. Some commonly-used loss functions, such as Cross Entropy (CE), suffer fr…
Deep Learning-based Face Super-Resolution: A Survey
Junjun Jiang, Chenyang Wang, Xianming Liu +1
Face super-resolution (FSR), also known as face hallucination, which is aimed at enhancing the resolution of low-resolution (LR) face images to generate high-resolution (HR) face i…