10 papers
NimbusGS: Unified 3D Scene Reconstruction under Hybrid Weather
Yanying Li, Jinyang Li, Shengfeng He +3
We present NimbusGS, a unified framework for reconstructing high-quality 3D scenes from degraded multi-view inputs captured under diverse and mixed adverse weather conditions. Unli…
OmniVTON++: Training-Free Universal Virtual Try-On with Principal Pose Guidance
Zhaotong Yang, Yong Du, Shengfeng He +5
Image-based Virtual Try-On (VTON) concerns the synthesis of realistic person imagery through garment re-rendering under human pose and body constraints. In practice, however, exist…
DeshadowMamba: Deshadowing as 1D Sequential Similarity
Zhaotong Yang, Yi Chen, Yanying Li +5
Recent deep models for image shadow removal often rely on attention-based architectures to capture long-range dependencies. However, their fixed attention patterns tend to mix illu…
AvatarVTON: 4D Virtual Try-On for Animatable Avatars
Zicheng Jiang, Jixin Gao, Shengfeng He +5
We propose AvatarVTON, the first 4D virtual try-on framework that generates realistic try-on results from a single in-shop garment image, enabling free pose control, novel-view ren…
HarmonPaint: Harmonized Training-Free Diffusion Inpainting
Ying Li, Xinzhe Li, Yong Du +3
Existing inpainting methods often require extensive retraining or fine-tuning to integrate new content seamlessly, yet they struggle to maintain coherence in both structure and sty…
OmniVTON: Training-Free Universal Virtual Try-On
Zhaotong Yang, Yuhui Li, Shengfeng He +4
Image-based Virtual Try-On (VTON) techniques rely on either supervised in-shop approaches, which ensure high fidelity but struggle with cross-domain generalization, or unsupervised…
PersonaMagic: Stage-Regulated High-Fidelity Face Customization with Tandem Equilibrium
Xinzhe Li, Jiahui Zhan, Shengfeng He +4
Personalized image generation has made significant strides in adapting content to novel concepts. However, a persistent challenge remains: balancing the accurate reconstruction of…
D-VTON: Dynamic Semantics Disentangling for Differential Diffusion based Virtual Try-On
Zhaotong Yang, Zicheng Jiang, Xinzhe Li +4
In this paper, we introduce D-VTON, an innovative solution for image-based virtual try-on. We address challenges from previous studies, such as semantic inconsistencies before…
High-order Neighborhoods Know More: HyperGraph Learning Meets Source-free Unsupervised Domain Adaptation
Jinkun Jiang, Qingxuan Lv, Yuezun Li +4
Source-free Unsupervised Domain Adaptation (SFDA) aims to classify target samples by only accessing a pre-trained source model and unlabelled target samples. Since no source data i…
One-for-All: Towards Universal Domain Translation with a Single StyleGAN
Yong Du, Jiahui Zhan, Xinzhe Li +4
In this paper, we propose a novel translation model, UniTranslator, for transforming representations between visually distinct domains under conditions of limited training data and…