1 citations · 1 across the 14 of their papers we have counts for
18 papers
BluePixel: Toward Native 4K Agentic Image Editing
Shanyan Guan, Shiyu Li, Xin Li +17
Instruction-driven image editing has reached native 4K, yet how such editors are trained remains undisclosed, and pretraining one from scratch is beyond most teams. The task itself…
VideoX-Qwen: Data-Centric Instruction-Based Video Editing
JJiahang Li, Dingbao Shao, Xinyu Chen +8
Progress in general-purpose video editing depends on constructing large-scale paired supervision and effectively adapting video-generation backbones to instruction-driven editing.…
RoMeRL: Balancing Feedback Coverage and the Memory-Reward Trap in Self-Evolving Agent Memory via Reduced-Order Utility States
Yi Yang, Zhennan Chen, Yihong Zhuang +5
Learning-based memory systems for self-evolving LLM agents face two tightly coupled challenges. First, trajectory-indexed utilities grow with the interaction history, thereby dispe…
SynVAR: Synergizing Spatial and Semantic Alignment in Visual Autoregressive Model
Zhennan Chen, Tianxing Shi, Pengcheng Xu +5
VAR has gained widespread popularity due to its next-scale prediction paradigm. However, it faces substantial performance bottlenecks when handling complex scenes with multiple obj…
Quo Vadis, World Modeling?
Yu Yang, Xuemeng Yang, Licheng Wen +17
Continually improving agents require dynamic interaction feedback beyond static supervision, yet direct real-environment interaction is costly, slow, unsafe, and hard to paralleliz…
Spiking Pyramid Wavelet Transformation for High-efficient and Low-energy Image Restoration
Chen Zhao, Xiantao Hu, Song Wu +5
Spiking neural networks (SNNs) have garnered significant interest in computer vision due to their potential for efficiency and biological inspiration. While spiking CNN-based metho…
VINS-120K: Ultra High-Resolution Image Editing with A Large-Scale Dataset
Zhizhou Chen, Shanyan Guan, Zhanxin Gao +6
Directly editing ultra-high-resolution (UHR) images is valuable but underexplored, primarily due to the lack of high-quality data and the challenge in modeling high-frequency textu…
L2P: Unlocking Latent Potential for Pixel Generation
Zhennan Chen, Junwei Zhu, Xu Chen +7
Pixel diffusion models have recently regained attention for visual generation. However, training advanced pixel-space models from scratch demands prohibitive computational and data…
TripVVT: A Large-Scale Triplet Dataset and a Coarse-Mask Baseline for In-the-Wild Video Virtual Try-On
Dingbao Shao, Song Wu, Shenyi Wang +9
Due to the scarcity of large-scale in-the-wild triplet data and the improper use of masks, the performance of video virtual try-on models remains limited. In this paper, we first i…
From Zero to Detail: A Progressive Spectral Decoupling Paradigm for UHD Image Restoration with New Benchmark
Chen Zhao, Yunzhe Xu, Zhizhou Chen +5
Ultra-high-definition (UHD) image restoration poses unique challenges due to the high spatial resolution, diverse content, and fine-grained structures present in UHD images. To add…
LUVE : Latent-Cascaded Ultra-High-Resolution Video Generation with Dual Frequency Experts
Chen Zhao, Jiawei Chen, Hongyu Li +6
Recent advances in video diffusion models have significantly improved visual quality, yet ultra-high-resolution (UHR) video generation remains a formidable challenge due to the com…
UltraHR-100K: Enhancing UHR Image Synthesis with A Large-Scale High-Quality Dataset
Chen Zhao, En Ci, Yunzhe Xu +5
Ultra-high-resolution (UHR) text-to-image (T2I) generation has seen notable progress. However, two key challenges remain : 1) the absence of a large-scale high-quality UHR T2I data…
Describe, Don't Dictate: Semantic Image Editing with Natural Language Intent
En Ci, Shanyan Guan, Yanhao Ge +5
Despite the progress in text-to-image generation, semantic image editing remains a challenge. Inversion-based algorithms unavoidably introduce reconstruction errors, while instruct…
Reverse Convolution and Its Applications to Image Restoration
Xuhong Huang, Shiqi Liu, Kai Zhang +4
Convolution and transposed convolution are fundamental operators widely used in neural networks. However, transposed convolution (a.k.a. deconvolution) does not serve as a true inv…
Learning Multi-scale Spatial-frequency Features for Image Denoising
Xu Zhao, Chen Zhao, Xiantao Hu +3
Recent advancements in multi-scale architectures have demonstrated exceptional performance in image denoising tasks. However, existing architectures mainly depends on a fixed singl…
From Zero to Detail: Deconstructing Ultra-High-Definition Image Restoration from Progressive Spectral Perspective
Chen Zhao, Zhizhou Chen, Yunzhe Xu +6
Ultra-high-definition (UHD) image restoration faces significant challenges due to its high resolution, complex content, and intricate details. To cope with these challenges, we ana…
Region-Aware Text-to-Image Generation via Hard Binding and Soft Refinement
Zhennan Chen, Yajie Li, Haofan Wang +6
Regional prompting, or compositional generation, which enables fine-grained spatial control, has gained increasing attention for its practicality in real-world applications. Howeve…
HybridBooth: Hybrid Prompt Inversion for Efficient Subject-Driven Generation
Shanyan Guan, Yanhao Ge, Ying Tai +3
Recent advancements in text-to-image diffusion models have shown remarkable creative capabilities with textual prompts, but generating personalized instances based on specific subj…