3 citations · 7 across the 33 of their papers we have counts for
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TurboClear: One-Step Object-Effect Removal via Region-Calibrated Distribution Matching and Fusion
Jiawei Guo, Junxian Li, Yixin Tang +4
Recently, diffusion-based removal methods have achieved promising visual quality in removing both target objects and their associated effects. However, they typically rely on multi…
PermuQuant: Lowering Per-Group Quantization Error by Reordering Channels for Diffusion Models
Yongsen Cheng, Kai Liu, Kaiwen Tao +5
Large-scale visual generative models have achieved remarkable performance. However, their high computational and memory costs make deployment challenging in resource-constrained sc…
GHOST: Geometry-Hierarchical Online Streaming Token Eviction for Efficient 3D Reconstruction
Leyang Chen, Junyi Wu, Zhiteng Li +1
Streaming 3D reconstruction from long monocular video sequences requires maintaining a key-value (KV) cache that grows linearly with sequence length, creating a severe memory bottl…
GTR: Generation-Guided Visual Token Reduction for Separate-Encoder Unified Multimodal Models
Junxian Li, Kai Liu, Zizhong Ding +4
The development of separate-encoder Unified multimodal models (UMMs) comes with a rapidly growing inference cost due to dense visual token processing. In this paper, we focus on un…
PVRF: All-in-one Adverse Weather Removal via Prior-modulated and Velocity-constrained Rectified Flow
Wei Dong, Han Zhou, Terry Ji +6
Adverse weather removal (AWR) in real-world images remains challenging due to heterogeneous and unseen degradations, while distortion-driven training often yields overly smooth res…
DiffST: Spatiotemporal-Aware Diffusion for Real-World Space-Time Video Super-Resolution
Zheng Chen, Ruofan Yang, Jin Han +5
Diffusion-based models have shown strong performance in video super-resolution (VSR) and video frame interpolation (VFI). However, their role in the coupled space-time video super-…
PRISM: Prior Rectification and Uncertainty-Aware Structure Modeling for Diffusion-Based Text Image Super-Resolution
Zihang Xu, Xiaoyang Liu, Zheng Chen +2
Text image super-resolution (Text-SR) requires more than visually plausible detail synthesis: slight errors in stroke topology may alter character identity and break readability. E…
FlashClear: Ultra-Fast Image Content Removal via Efficient Step Distillation and Feature Caching
Yixin Tang, Jiawei Guo, Junxian Li +6
Recently, diffusion-based object removal models have achieved impressive results in eliminating objects and their associated visual effects. However, they indiscriminately denoise…
InsHuman: Towards Natural and Identity-Preserving Human Insertion
Jie Li, Shulian Zhang, Yangyang Gao +4
Human insertion aims to naturally place specific individuals into a target background. Although existing image editing models may have such ability, they often produce failure case…
LVOmniBench: Pioneering Long Audio-Video Understanding Evaluation for Omnimodal LLMs
Keda Tao, Yuhua Zheng, Jia Xu +13
Recent advancements in omnimodal large language models (OmniLLMs) have significantly improved the comprehension of audio and video inputs. However, current evaluations primarily fo…
Efficient Video Diffusion with Sparse Information Transmission for Video Compression
Mingde Zhou, Zheng Chen, Yulun Zhang
Video compression aims to maximize reconstruction quality with minimal bitrates. Beyond standard distortion metrics, perceptual quality and temporal consistency are also critical.…
LSGQuant: Layer-Sensitivity Guided Quantization for One-Step Diffusion Real-World Video Super-Resolution
Tianxing Wu, Zheng Chen, Cirou Xu +5
One-Step Diffusion Models have demonstrated promising capability and fast inference in video super-resolution (VSR) for real-world. Nevertheless, the substantial model size and hig…
BinaryDemoire: Moiré-Aware Binarization for Image Demoiréing
Zheng Chen, Zhi Yang, Xiaoyang Liu +5
Image demoiréing aims to remove structured moiré artifacts in recaptured imagery, where degradations are highly frequency-dependent and vary across scales and directions. While rec…
Q-DiT4SR: Exploration of Detail-Preserving Diffusion Transformer Quantization for Real-World Image Super-Resolution
Xun Zhang, Kaicheng Yang, Hongliang Lu +3
Recently, Diffusion Transformers (DiTs) have emerged in Real-World Image Super-Resolution (Real-ISR) to generate high-quality textures, yet their heavy inference burden hinders rea…
UnfoldLDM: Degradation-Aware Unfolding with Iterative Latent Diffusion Priors for Blind Image Restoration
Chunming He, Rihan Zhang, Zheng Chen +6
Deep unfolding networks (DUNs) combine the interpretability of model-based methods with the learning ability of deep networks, yet remain limited for blind image restoration (BIR).…
Asymmetric VAE for One-Step Video Super-Resolution Acceleration
Jianze Li, Yong Guo, Yulun Zhang +1
Diffusion models have significant advantages in the field of real-world video super-resolution and have demonstrated strong performance in past research. In recent diffusion-based…
Towards Redundancy Reduction in Diffusion Models for Efficient Video Super-Resolution
Jinpei Guo, Yifei Ji, Shengwei Wang +8
Diffusion models have recently shown promising results for video super-resolution (VSR). However, directly adapting generative diffusion models to VSR can result in redundancy, sin…
VividFace: High-Quality and Efficient One-Step Diffusion For Video Face Enhancement
Shulian Zhang, Yong Guo, Long Peng +6
Video Face Enhancement (VFE) aims to restore high-quality facial regions from degraded video sequences, enabling a wide range of practical applications. Despite substantial progres…
SODiff: Semantic-Oriented Diffusion Model for JPEG Compression Artifacts Removal
Tingyu Yang, Jue Gong, Jinpei Guo +3
JPEG, as a widely used image compression standard, often introduces severe visual artifacts when achieving high compression ratios. Although existing deep learning-based restoratio…
Steering One-Step Diffusion Model with Fidelity-Rich Decoder for Fast Image Compression
Zheng Chen, Mingde Zhou, Jinpei Guo +3
Diffusion-based image compression has demonstrated impressive perceptual performance. However, it suffers from two critical drawbacks: (1) excessive decoding latency due to multi-s…
QuantVSR: Low-Bit Post-Training Quantization for Real-World Video Super-Resolution
Bowen Chai, Zheng Chen, Libo Zhu +3
Diffusion models have shown superior performance in real-world video super-resolution (VSR). However, the slow processing speeds and heavy resource consumption of diffusion models…
PMQ-VE: Progressive Multi-Frame Quantization for Video Enhancement
ZhanFeng Feng, Long Peng, Xin Di +7
Multi-frame video enhancement tasks aim to improve the spatial and temporal resolution and quality of video sequences by leveraging temporal information from multiple frames, which…
DOVE: Efficient One-Step Diffusion Model for Real-World Video Super-Resolution
Zheng Chen, Zichen Zou, Kewei Zhang +4
Diffusion models have demonstrated promising performance in real-world video super-resolution (VSR). However, the dozens of sampling steps they require, make inference extremely sl…
QArtSR: Quantization via Reverse-Module and Timestep-Retraining in One-Step Diffusion based Image Super-Resolution
Libo Zhu, Haotong Qin, Kaicheng Yang +5
One-step diffusion-based image super-resolution (OSDSR) models are showing increasingly superior performance nowadays. However, although their denoising steps are reduced to one an…