95 citations · 194 across the 18 of their papers we have counts for
19 papers
DreamO: A Unified Framework for Image Customization
Chong Mou, Yanze Wu, Wenxu Wu +15
Recently, extensive research on image customization (e.g., identity, subject, style, background, etc.) demonstrates strong customization capabilities in large-scale generative mode…
Consistent Human Image and Video Generation with Spatially Conditioned Diffusion
Mingdeng Cao, Chong Mou, Ziyang Yuan +4
Consistent human-centric image and video synthesis aims to generate images or videos with new poses while preserving appearance consistency with a given reference image, which is c…
OmniDrag: Enabling Motion Control for Omnidirectional Image-to-Video Generation
Weiqi Li, Shijie Zhao, Chong Mou +6
As virtual reality gains popularity, the demand for controllable creation of immersive and dynamic omnidirectional videos (ODVs) is increasing. While previous text-to-ODV generatio…
ReVideo: Remake a Video with Motion and Content Control
Chong Mou, Mingdeng Cao, Xintao Wang +3
Despite significant advancements in video generation and editing using diffusion models, achieving accurate and localized video editing remains a substantial challenge. Additionall…
Diffusion-Based Hierarchical Image Steganography
Youmin Xu, Xuanyu Zhang, Jiwen Yu +3
This paper introduces Hierarchical Image Steganography, a novel method that enhances the security and capacity of embedding multiple images into a single container using diffusion…
DiffEditor: Boosting Accuracy and Flexibility on Diffusion-based Image Editing
Chong Mou, Xintao Wang, Jiechong Song +2
Large-scale Text-to-Image (T2I) diffusion models have revolutionized image generation over the last few years. Although owning diverse and high-quality generation capabilities, tra…
360DVD: Controllable Panorama Video Generation with 360-Degree Video Diffusion Model
Qian Wang, Weiqi Li, Chong Mou +2
Panorama video recently attracts more interest in both study and application, courtesy of its immersive experience. Due to the expensive cost of capturing 360-degree panoramic vide…
Neural Video Fields Editing
Shuzhou Yang, Chong Mou, Jiwen Yu +3
Diffusion models have revolutionized text-driven video editing. However, applying these methods to real-world editing encounters two significant challenges: (1) the rapid increase…
DragonDiffusion: Enabling Drag-style Manipulation on Diffusion Models
Chong Mou, Xintao Wang, Jiechong Song +2
Despite the ability of existing large-scale text-to-image (T2I) models to generate high-quality images from detailed textual descriptions, they often lack the ability to precisely…
Optimization-Inspired Cross-Attention Transformer for Compressive Sensing
Jiechong Song, Chong Mou, Shiqi Wang +2
By integrating certain optimization solvers with deep neural networks, deep unfolding network (DUN) with good interpretability and high performance has attracted growing attention…
Large-capacity and Flexible Video Steganography via Invertible Neural Network
Chong Mou, Youmin Xu, Jiechong Song +3
Video steganography is the art of unobtrusively concealing secret data in a cover video and then recovering the secret data through a decoding protocol at the receiver end. Althoug…
T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models
Chong Mou, Xintao Wang, Liangbin Xie +5
The incredible generative ability of large-scale text-to-image (T2I) models has demonstrated strong power of learning complex structures and meaningful semantics. However, relying…
TransCL: Transformer Makes Strong and Flexible Compressive Learning
Chong Mou, Jian Zhang
Compressive learning (CL) is an emerging framework that integrates signal acquisition via compressed sensing (CS) and machine learning for inference tasks directly on a small numbe…
MM-RealSR: Metric Learning based Interactive Modulation for Real-World Super-Resolution
Chong Mou, Yanze Wu, Xintao Wang +3
Interactive image restoration aims to restore images by adjusting several controlling coefficients, which determine the restoration strength. Existing methods are restricted in lea…
Deep Generalized Unfolding Networks for Image Restoration
Chong Mou, Qian Wang, Jian Zhang
Deep neural networks (DNN) have achieved great success in image restoration. However, most DNN methods are designed as a black box, lacking transparency and interpretability. Altho…
Dynamic Attentive Graph Learning for Image Restoration
Chong Mou, Jian Zhang, Zhuoyuan Wu
Non-local self-similarity in natural images has been verified to be an effective prior for image restoration. However, most existing deep non-local methods assign a fixed number of…
Dense Deep Unfolding Network with 3D-CNN Prior for Snapshot Compressive Imaging
Zhuoyuan Wu, Jian Zhang, Chong Mou
Snapshot compressive imaging (SCI) aims to record three-dimensional signals via a two-dimensional camera. For the sake of building a fast and accurate SCI recovery algorithm, we in…
NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results
Ren Yang, Radu Timofte, Jing Liu +69
This paper reviews the first NTIRE challenge on quality enhancement of compressed video, with a focus on the proposed methods and results. In this challenge, the new Large-scale Di…
COLA-Net: Collaborative Attention Network for Image Restoration
Chong Mou, Jian Zhang, Xiaopeng Fan +2
Local and non-local attention-based methods have been well studied in various image restoration tasks while leading to promising performance. However, most of the existing methods…