1 citations · 4 across the 16 of their papers we have counts for
16 papers
FFP-300K: Scaling First-Frame Propagation for Generalizable Video Editing
Xijie Huang, Chengming Xu, Donghao Luo +6
First-Frame Propagation (FFP) offers a promising paradigm for controllable video editing, but existing methods are hampered by a reliance on cumbersome run-time guidance. We identi…
SwiftVideo: A Unified Framework for Few-Step Video Generation through Trajectory-Distribution Alignment
Yanxiao Sun, Jiafu Wu, Yun Cao +6
Diffusion-based or flow-based models have achieved significant progress in video synthesis but require multiple iterative sampling steps, which incurs substantial computational ove…
StrandDesigner: Towards Practical Strand Generation with Sketch Guidance
Na Zhang, Moran Li, Chengming Xu +6
Realistic hair strand generation is crucial for applications like computer graphics and virtual reality. While diffusion models can generate hairstyles from text or images, these i…
Visual Document Understanding and Reasoning: A Multi-Agent Collaboration Framework with Agent-Wise Adaptive Test-Time Scaling
Xinlei Yu, Chengming Xu, Zhangquan Chen +6
The dominant paradigm of monolithic scaling in Vision-Language Models (VLMs) is failing for understanding and reasoning in documents, yielding diminishing returns as it struggles w…
VTBench: Comprehensive Benchmark Suite Towards Real-World Virtual Try-on Models
Hu Xiaobin, Liang Yujie, Luo Donghao +5
While virtual try-on has achieved significant progress, evaluating these models towards real-world scenarios remains a challenge. A comprehensive benchmark is essential for three k…
Disentangle Identity, Cooperate Emotion: Correlation-Aware Emotional Talking Portrait Generation
Weipeng Tan, Chuming Lin, Chengming Xu +6
Recent advances in Talking Head Generation (THG) have achieved impressive lip synchronization and visual quality through diffusion models; yet existing methods struggle to generate…
When Preferences Diverge: Aligning Diffusion Models with Minority-Aware Adaptive DPO
Lingfan Zhang, Chen Liu, Chengming Xu +5
In recent years, the field of image generation has witnessed significant advancements, particularly in fine-tuning methods that align models with universal human preferences. This…
CrossVTON: Mimicking the Logic Reasoning on Cross-category Virtual Try-on guided by Tri-zone Priors
Donghao Luo, Yujie Liang, Xu Peng +6
Despite remarkable progress in image-based virtual try-on systems, generating realistic and robust fitting images for cross-category virtual try-on remains a challenging task. The…
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…