8 citations · 15 across the 23 of their papers we have counts for
27 papers
ZoomDiff: A High-Fidelity Diffusion Model for Dual-Camera Smooth Zooming
Jiayi Zhang, Renlong Wu, Yukang Ding +2
Digital zoom transitions between dual cameras often exhibit conspicuous discontinuities in geometric structure and chromatic consistency, degrading the user experience. While recen…
SATB-VR: Training Few-Step Video Restoration Diffusion Model using SNR-Aware Trajectory Blending
Haoran Bai, Xiaoxu Chen, Xiaoyu Liu +4
While diffusion models excel in video restoration, their reliance on extensive iterative steps limits efficiency. Conversely, aggressive single-step distillation often compromises…
TextLDM: Language Modeling with Continuous Latent Diffusion
Jiaxiu Jiang, Jingjing Ren, Wenbo Li +10
Diffusion Transformers (DiT) trained with flow matching in a VAE latent space have unified visual generation across images and videos. A natural next step toward a single architect…
Auto-FlexSwitch: Efficient Dynamic Model Merging via Learnable Task Vector Compression
Junqi Gao, Dazhi Zhang, Zhichang Guo +3
Model merging has attracted attention as an effective path toward multi-task adaptation by integrating knowledge from multiple task-specific models. Among existing approaches, dyna…
RoomEditor++: A Parameter-Sharing Diffusion Architecture for High-Fidelity Furniture Synthesis
Qilong Wang, Xiaofan Ming, Zhenyi Lin +4
Virtual furniture synthesis, which seamlessly integrates reference objects into indoor scenes while maintaining geometric coherence and visual realism, holds substantial promise fo…
QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation
Jiahui Yang, Yongjia Ma, Donglin Di +6
Existing text-to-image models often rely on parameter fine-tuning techniques such as Low-Rank Adaptation (LoRA) to customize visual attributes. However, when combining multiple LoR…
Rethinking Cross-Modal Interaction in Multimodal Diffusion Transformers
Zhengyao Lv, Tianlin Pan, Chenyang Si +4
Multimodal Diffusion Transformers (MM-DiTs) have achieved remarkable progress in text-driven visual generation. However, even state-of-the-art MM-DiT models like FLUX struggle with…
LoViC: Efficient Long Video Generation with Context Compression
Jiaxiu Jiang, Wenbo Li, Jingjing Ren +5
Despite recent advances in diffusion transformers (DiTs) for text-to-video generation, scaling to long-duration content remains challenging due to the quadratic complexity of self-…
Motion-Aware Adaptive Pixel Pruning for Efficient Local Motion Deblurring
Wei Shang, Dongwei Ren, Wanying Zhang +3
Local motion blur in digital images originates from the relative motion between dynamic objects and static imaging systems during exposure. Existing deblurring methods face signifi…
High-Frequency Prior-Driven Adaptive Masking for Accelerating Image Super-Resolution
Wei Shang, Dongwei Ren, Wanying Zhang +3
The primary challenge in accelerating image super-resolution lies in reducing computation while maintaining performance and adaptability. Motivated by the observation that high-fre…
Physics Consistency and Latent Dynamics in Spatiotemporal Physics Field Generation
Peimian Du, Jiabin Liu, Xiaowei Jin +2
Data-driven models for spatiotemporal physical field generation, such as flow and acoustic fields, often deviate from governing equations and lack interpretability in latent tempor…
Bridging Geometry-Coherent Text-to-3D Generation with Multi-View Diffusion Priors and Gaussian Splatting
Feng Yang, Wenliang Qian, Wangmeng Zuo +1
Score Distillation Sampling (SDS) leverages pretrained 2D diffusion models to advance text-to-3D generation but neglects multi-view correlations, being prone to geometric inconsist…
FramePainter: Endowing Interactive Image Editing with Video Diffusion Priors
Yabo Zhang, Xinpeng Zhou, Yihan Zeng +3
Interactive image editing allows users to modify images through visual interaction operations such as drawing, clicking, and dragging. Existing methods construct such supervision s…
Generative Inbetweening through Frame-wise Conditions-Driven Video Generation
Tianyi Zhu, Dongwei Ren, Qilong Wang +2
Generative inbetweening aims to generate intermediate frame sequences by utilizing two key frames as input. Although remarkable progress has been made in video generation models, g…
Class Balance Matters to Active Class-Incremental Learning
Zitong Huang, Ze Chen, Yuanze Li +6
Few-Shot Class-Incremental Learning has shown remarkable efficacy in efficient learning new concepts with limited annotations. Nevertheless, the heuristic few-shot annotations may…
Seeing Beyond Views: Multi-View Driving Scene Video Generation with Holistic Attention
Hannan Lu, Xiaohe Wu, Shudong Wang +5
Generating multi-view videos for autonomous driving training has recently gained much attention, with the challenge of addressing both cross-view and cross-frame consistency. Exist…
Combining Generative and Geometry Priors for Wide-Angle Portrait Correction
Lan Yao, Chaofeng Chen, Xiaoming Li +2
Wide-angle lens distortion in portrait photography presents a significant challenge for capturing photo-realistic and aesthetically pleasing images. Such distortions are especially…
VitaGlyph: Vitalizing Artistic Typography with Flexible Dual-branch Diffusion Models
Kailai Feng, Yabo Zhang, Haodong Yu +4
Artistic typography is a technique to visualize the meaning of input character in an imaginable and readable manner. With powerful text-to-image diffusion models, existing methods…
Reblurring-Guided Single Image Defocus Deblurring: A Learning Framework with Misaligned Training Pairs
Dongwei Ren, Xinya Shu, Yu Li +3
For single image defocus deblurring, acquiring well-aligned training pairs (or training triplets), i.e., a defocus blurry image, an all-in-focus sharp image (and a defocus blur map…
SelfDRSC++: Self-Supervised Dual Reversed Rolling Shutter Correction via Video Interpolation
Wei Shang, Dongwei Ren, Wanying Zhang +3
Modern consumer cameras often use rolling shutter, capturing scenes row-by-row and causing distortion in dynamic scenes. Existing correction methods rely on supervised learning wit…
Evaluation of Text-to-Video Generation Models: A Dynamics Perspective
Mingxiang Liao, Hannan Lu, Xinyu Zhang +6
Comprehensive and constructive evaluation protocols play an important role in the development of sophisticated text-to-video (T2V) generation models. Existing evaluation protocols…
Diffusion Models in Low-Level Vision: A Survey
Chunming He, Yuqi Shen, Chengyu Fang +6
Deep generative models have garnered significant attention in low-level vision tasks due to their generative capabilities. Among them, diffusion model-based solutions, characterize…
NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment: Methods and Results
Xin Li, Kun Yuan, Yajing Pei +65
This paper reviews the NTIRE 2024 Challenge on Shortform UGC Video Quality Assessment (S-UGC VQA), where various excellent solutions are submitted and evaluated on the collected da…
MC: Multi-concept Guidance for Customized Multi-concept Generation
Jiaxiu Jiang, Yabo Zhang, Kailai Feng +5
Customized text-to-image generation, which synthesizes images based on user-specified concepts, has made significant progress in handling individual concepts. However, when extende…