1 citations · 4 across the 16 of their papers we have counts for
8 papers · 1 filter
FitDiT: Advancing the Authentic Garment Details for High-fidelity Virtual Try-on
Boyuan Jiang, Xiaobin Hu, Donghao Luo +7
Although image-based virtual try-on has made considerable progress, emerging approaches still encounter challenges in producing high-fidelity and robust fitting images across diver…
SVP: Style-Enhanced Vivid Portrait Talking Head Diffusion Model
Weipeng Tan, Chuming Lin, Chengming Xu +5
Talking Head Generation (THG), typically driven by audio, is an important and challenging task with broad application prospects in various fields such as digital humans, film produ…
EFCNet: Every Feature Counts for Small Medical Object Segmentation
Lingjie Kong, Qiaoling Wei, Chengming Xu +2
This paper explores the segmentation of very small medical objects with significant clinical value. While Convolutional Neural Networks (CNNs), particularly UNet-like models, and r…
CustAny: Customizing Anything from A Single Example
Lingjie Kong, Kai Wu, Xiaobin Hu +8
Recent advances in diffusion-based text-to-image models have simplified creating high-fidelity images, but preserving the identity (ID) of specific elements, like a personal dog, i…
VividPose: Advancing Stable Video Diffusion for Realistic Human Image Animation
Qilin Wang, Zhengkai Jiang, Chengming Xu +7
Human image animation involves generating a video from a static image by following a specified pose sequence. Current approaches typically adopt a multi-stage pipeline that separat…
A Generalization Theory of Cross-Modality Distillation with Contrastive Learning
Hangyu Lin, Chen Liu, Chengming Xu +3
Cross-modality distillation arises as an important topic for data modalities containing limited knowledge such as depth maps and high-quality sketches. Such techniques are of great…
Towards Global Optimal Visual In-Context Learning Prompt Selection
Chengming Xu, Chen Liu, Yikai Wang +2
Visual In-Context Learning (VICL) is a prevailing way to transfer visual foundation models to new tasks by leveraging contextual information contained in in-context examples to enh…
DiffFAE: Advancing High-fidelity One-shot Facial Appearance Editing with Space-sensitive Customization and Semantic Preservation
Qilin Wang, Jiangning Zhang, Chengming Xu +7
Facial Appearance Editing (FAE) aims to modify physical attributes, such as pose, expression and lighting, of human facial images while preserving attributes like identity and back…