34 citations · 53 across the 16 of their papers we have counts for
11 papers · 1 filter
VIEScore2: Unified Image Evaluation with Spatially Grounded Explanations
Xianda Du, Max Ku, Weiming Ren +5
Existing synthetic image evaluators typically provide only a scalar quality score and do not identify the image regions that support it. We introduce VIEScore2, a unified evaluator…
PhyProbe: Rethinking Physical Consistency Evaluation in Generated Videos
Max Ku, Jiaojiao Fan, Zekun Hao +5
Evaluating the physical consistency of generated videos remains a fundamental challenge. Existing approaches rely on off-the-shelf vision-language models, which can often be myopic…
VisPhyWorld: Probing Physical Reasoning via Code-Driven Video Reconstruction
Jiarong Liang, Max Ku, Ka-Hei Hui +2
Evaluating whether Multimodal Large Language Models (MLLMs) genuinely reason about physical dynamics remains challenging. Most existing benchmarks rely on recognition-style protoco…
EditReward: A Human-Aligned Reward Model for Instruction-Guided Image Editing
Keming Wu, Sicong Jiang, Max Ku +3
Recently, we have witnessed great progress in image editing with natural language instructions. Several closed-source models like GPT-Image-1, Seedream, and Google-Nano-Banana have…
VideoScore: Building Automatic Metrics to Simulate Fine-grained Human Feedback for Video Generation
Xuan He, Dongfu Jiang, Ge Zhang +16
The recent years have witnessed great advances in video generation. However, the development of automatic video metrics is lagging significantly behind. None of the existing metric…
MANTIS: Interleaved Multi-Image Instruction Tuning
Dongfu Jiang, Xuan He, Huaye Zeng +4
Large multimodal models (LMMs) have shown great results in single-image vision language tasks. However, their abilities to solve multi-image visual language tasks is yet to be impr…
AnyV2V: A Tuning-Free Framework For Any Video-to-Video Editing Tasks
Max Ku, Cong Wei, Weiming Ren +2
In the dynamic field of digital content creation using generative models, state-of-the-art video editing models still do not offer the level of quality and control that users desir…
VIEScore: Towards Explainable Metrics for Conditional Image Synthesis Evaluation
Max Ku, Dongfu Jiang, Cong Wei +2
In the rapidly advancing field of conditional image generation research, challenges such as limited explainability lie in effectively evaluating the performance and capabilities of…
ImagenHub: Standardizing the evaluation of conditional image generation models
Max Ku, Tianle Li, Kai Zhang +4
Recently, a myriad of conditional image generation and editing models have been developed to serve different downstream tasks, including text-to-image generation, text-guided image…
DreamEdit: Subject-driven Image Editing
Tianle Li, Max Ku, Cong Wei +1
Subject-driven image generation aims at generating images containing customized subjects, which has recently drawn enormous attention from the research community. However, the prev…
Intelligent Painter: Picture Composition With Resampling Diffusion Model
Wing-Fung Ku, Wan-Chi Siu, Xi Cheng +1
Have you ever thought that you can be an intelligent painter? This means that you can paint a picture with a few expected objects in mind, or with a desirable scene. This is differ…