2 citations · 8 across the 29 of their papers we have counts for
22 papers · 1 filter
ExeWorldBench: Benchmarking Code-Based Executable 3D World Modeling
Xinge Peng, Zhen Fang, Ruibing Wang +8
Coding agents are increasingly evolving beyond reproducing what a world looks like toward constructing executable worlds that capture how a world works. This shift calls for execut…
SpongeBob: Sync-Aware Harmonious Audio-Visual Generative Editing
Sen Liang, Cong Wang, Fengbin Guan +6
Visual and acoustic events in the physical world are inherently coupled, yet existing video editing methods typically adopt decoupled pipelines, lacking bidirectional modality inte…
IQA-Spider: Unifying Multi-Granularity Image Quality Assessment with Reasoning, Grounding and Referring
Xinge Peng, Yiting Lu, Xin Li +1
We present IQA-Spider, the first image quality assessment (IQA) framework that unifies reasoning, grounding, and referring into a single LMM-based framework for multi-granularity q…
LoViF 2026 The First Challenge on Holistic Quality Assessment for 4D World Model (PhyScore)
Wei Luo, Yiting Lu, Xin Li +32
This paper reports on the LoViF 2026 PhyScore challenge, a competition on holistic quality assessment of world-model-generated videos across both 2D and 4D generation settings. The…
DiffCap-Bench: A Comprehensive, Challenging, Robust Benchmark for Image Difference Captioning
Yuancheng Wei, Haojie Zhang, Linli Yao +7
Image Difference Captioning (IDC) generates natural language descriptions that precisely identify differences between two images, serving as a key benchmark for fine-grained change…
4DWorldBench: A Comprehensive Evaluation Framework for 3D/4D World Generation Models
Yiting Lu, Wei Luo, Peiyan Tu +8
World Generation Models are emerging as a cornerstone of next-generation multimodal intelligence systems. Unlike traditional 2D visual generation, World Models aim to construct rea…
OmniQuality-R: Advancing Reward Models Through All-Encompassing Quality Assessment
Yiting Lu, Fengbin Guan, Yixin Gao +8
Current visual evaluation approaches are typically constrained to a single task. To address this, we propose OmniQuality-R, a unified reward modeling framework that transforms mult…
VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results
Dasong Li, Sizhuo Ma, Hang Hua +40
This paper presents an overview of the VQualA 2025 Challenge on Engagement Prediction for Short Videos, held in conjunction with ICCV 2025. The challenge focuses on understanding a…
Comp-X: On Defining an Interactive Learned Image Compression Paradigm With Expert-driven LLM Agent
Yixin Gao, Xin Li, Xiaohan Pan +7
We present Comp-X, the first intelligently interactive image compression paradigm empowered by the impressive reasoning capability of large language model (LLM) agent. Notably, com…
NTIRE 2025 XGC Quality Assessment Challenge: Methods and Results
Xiaohong Liu, Xiongkuo Min, Qiang Hu +92
This paper reports on the NTIRE 2025 XGC Quality Assessment Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) a…
OmniCaptioner: One Captioner to Rule Them All
Yiting Lu, Jiakang Yuan, Zhen Li +17
We propose OmniCaptioner, a versatile visual captioning framework for generating fine-grained textual descriptions across a wide variety of visual domains. Unlike prior methods lim…
NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment
Shuhao Han, Haotian Fan, Fangyuan Kong +112
This paper reports on the NTIRE 2025 challenge on Text to Image (T2I) generation model quality assessment, which will be held in conjunction with the New Trends in Image Restoratio…
Q-Adapt: Adapting LMM for Visual Quality Assessment with Progressive Instruction Tuning
Yiting Lu, Xin Li, Haoning Wu +3
The rapid advancement of Large Multi-modal Foundation Models (LMM) has paved the way for the possible Explainable Image Quality Assessment (EIQA) with instruction tuning from two p…
Lumina-Image 2.0: A Unified and Efficient Image Generative Framework
Qi Qin, Le Zhuo, Yi Xin +20
We introduce Lumina-Image 2.0, an advanced text-to-image generation framework that achieves significant progress compared to previous work, Lumina-Next. Lumina-Image 2.0 is built u…
Hybrid Agents for Image Restoration
Bingchen Li, Xin Li, Yiting Lu +1
Existing Image Restoration (IR) studies typically focus on task-specific or universal modes individually, relying on the mode selection of users and lacking the cooperation between…
IMAGINE-E: Image Generation Intelligence Evaluation of State-of-the-art Text-to-Image Models
Jiayi Lei, Renrui Zhang, Xiangfei Hu +12
With the rapid development of diffusion models, text-to-image(T2I) models have made significant progress, showcasing impressive abilities in prompt following and image generation.…
LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents
Bingchen Li, Xin Li, Yiting Lu +1
We present the first loss agent, dubbed LossAgent, for low-level image processing tasks, e.g., image super-resolution and restoration, intending to achieve any customized optimizat…
Priorformer: A UGC-VQA Method with content and distortion priors
Yajing Pei, Shiyu Huang, Yiting Lu +2
User Generated Content (UGC) videos are susceptible to complicated and variant degradations and contents, which prevents the existing blind video quality assessment (BVQA) models f…
QMamba: On First Exploration of Vision Mamba for Image Quality Assessment
Fengbin Guan, Xin Li, Zihao Yu +2
In this work, we take the first exploration of the recently popular foundation model, i.e., State Space Model/Mamba, in image quality assessment (IQA), aiming at observing and exca…
PromptCIR: Blind Compressed Image Restoration with Prompt Learning
Bingchen Li, Xin Li, Yiting Lu +5
Blind Compressed Image Restoration (CIR) has garnered significant attention due to its practical applications. It aims to mitigate compression artifacts caused by unknown quality f…
VisualCritic: Making LMMs Perceive Visual Quality Like Humans
Zhipeng Huang, Zhizheng Zhang, Yiting Lu +3
At present, large multimodal models (LMMs) have exhibited impressive generalization capabilities in understanding and generating visual signals. However, they currently still lack…
Video Quality Assessment Based on Swin TransformerV2 and Coarse to Fine Strategy
Zihao Yu, Fengbin Guan, Yiting Lu +2
The objective of non-reference video quality assessment is to evaluate the quality of distorted video without access to reference high-definition references. In this study, we intr…