activity
20232026
most citedDiffusionMat: Alpha Matting as Sequential Refinement Learning

1 citations · 1 across the 15 of their papers we have counts for

collaborators

21 papers

cs.CV2026

Bridging Event Streams and DiT: Event-Guided Video Frame Interpolation

Guixu Lin, Yuyang Yu, Xiang Ji +6

Latent diffusion models have recently advanced video frame interpolation by synthesizing intermediate frames between input images. However, handling large temporal gaps and complex…

cs.CV2026

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…

cs.CV2026

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…

cs.CV2026

MotionAdapter: Video Motion Transfer via Content-Aware Attention Customization

Zhexin Zhang, Yangyang Xu, Yifeng Zhu +4

Recent advances in diffusion-based text-to-video models, particularly those built on the diffusion transformer architecture, have achieved remarkable progress in generating high-qu…

cs.CV2025

FlashMesh: Faster and Better Autoregressive Mesh Synthesis via Structured Speculation

Tingrui Shen, Yiheng Zhang, Chen Tang +6

Autoregressive models can generate high-quality 3D meshes by sequentially producing vertices and faces, but their token-by-token decoding results in slow inference, limiting practi…

cs.CV2025

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…

cs.CV2025

Stroke2Sketch: Harnessing Stroke Attributes for Training-Free Sketch Generation

Rui Yang, Huining Li, Yiyi Long +2

Generating sketches guided by reference styles requires precise transfer of stroke attributes, such as line thickness, deformation, and texture sparsity, while preserving semantic…

cs.CV2025

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…

cs.CV2025

Cross-Subject Mind Decoding from Inaccurate Representations

Yangyang Xu, Bangzhen Liu, Wenqi Shao +3

Decoding stimulus images from fMRI signals has advanced with pre-trained generative models. However, existing methods struggle with cross-subject mappings due to cognitive variabil…

cs.CV2025

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…

cs.CV2025

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…

cs.CV2025

Stable Score Distillation

Haiming Zhu, Yangyang Xu, Chenshu Xu +5

Text-guided image and 3D editing have advanced with diffusion-based models, yet methods like Delta Denoising Score often struggle with stability, spatial control, and editing stren…

cs.MM2025

How Far Are We from Generating Missing Modalities with Foundation Models?

Guanzhou Ke, Bo Wang, Guoqing Chao +2

Multimodal foundation models have demonstrated impressive capabilities across diverse tasks. However, their potential as plug-and-play solutions for missing modality reconstruction…

cs.CV2025

Latent Wavelet Diffusion For Ultra-High-Resolution Image Synthesis

Luigi Sigillo, Shengfeng He, Danilo Comminiello

High-resolution image synthesis remains a core challenge in generative modeling, particularly in balancing computational efficiency with the preservation of fine-grained visual det…

cs.CV2025

Safe-Sora: Safe Text-to-Video Generation via Graphical Watermarking

Zihan Su, Xuerui Qiu, Hongbin Xu +6

The explosive growth of generative video models has amplified the demand for reliable copyright preservation of AI-generated content. Despite its popularity in image synthesis, inv…

cs.CV2025

Adversarial Attacks on Event-Based Pedestrian Detectors: A Physical Approach

Guixu Lin, Muyao Niu, Qingtian Zhu +4

Event cameras, known for their low latency and high dynamic range, show great potential in pedestrian detection applications. However, while recent research has primarily focused o…

cs.CV2024

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…

cs.CV2024

Task-Oriented Diffusion Inversion for High-Fidelity Text-based Editing

Yangyang Xu, Wenqi Shao, Yong Du +4

Recent advancements in text-guided diffusion models have unlocked powerful image manipulation capabilities, yet balancing reconstruction fidelity and editability for real images re…

cs.CV2024

Zero-Shot Video Translation via Token Warping

Haiming Zhu, Yangyang Xu, Jun Yu +1

With the revolution of generative AI, video-related tasks have been widely studied. However, current state-of-the-art video models still lag behind image models in visual quality a…

cs.CV2023★ 1 cited

DiffusionMat: Alpha Matting as Sequential Refinement Learning

Yangyang Xu, Shengfeng He, Wenqi Shao +3

In this paper, we introduce DiffusionMat, a novel image matting framework that employs a diffusion model for the transition from coarse to refined alpha mattes. Diverging from conv…

cs.CV2023

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…