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20242026
most citedWISE: A World Knowledge-Informed Semantic Evaluation for Text-to-Image Generation

4 citations · 6 across the 11 of their papers we have counts for

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cs.CV2026

Scaling Mixture-of-Experts Video Pretraining for Embodied Intelligence

Shuailei Ma, Jiaqi Liao, Xinyang Wang +24

Despite the recent promise in robot control, video generative models suffer from a domain mismatch due to their primary focus on content creation. For example, their design inheren…

cs.CV2026

ClawMark: A Living-World Benchmark for Multi-Turn, Multi-Day, Multimodal Coworker Agents

Fanqing Meng, Lingxiao Du, Zijian Wu +46

Language-model agents are increasingly used as persistent coworkers that assist users across multiple working days. During such workflows, the surrounding environment may change in…

cs.CV2026

Gym-V: A Unified Vision Environment System for Agentic Vision Research

Fanqing Meng, Lingxiao Du, Jiawei Gu +9

As agentic systems increasingly rely on reinforcement learning from verifiable rewards, standardized ``gym'' infrastructure has become essential for rapid iteration, reproducibilit…

cs.CV2025

Does Understanding Inform Generation in Unified Multimodal Models? From Analysis to Path Forward

Yuwei Niu, Weiyang Jin, Jiaqi Liao +7

Recent years have witnessed significant progress in Unified Multimodal Models, yet a fundamental question remains: Does understanding truly inform generation in Unified Multimodal…

cs.CV2025

SRUM: Fine-Grained Self-Rewarding for Unified Multimodal Models

Weiyang Jin, Yuwei Niu, Jiaqi Liao +4

Recently, remarkable progress has been made in Unified Multimodal Models (UMMs), which integrate vision-language generation and understanding capabilities within a single framework…

cs.CV2025

VideoREPA: Learning Physics for Video Generation through Relational Alignment with Foundation Models

Xiangdong Zhang, Jiaqi Liao, Shaofeng Zhang +4

Recent advancements in text-to-video (T2V) diffusion models have enabled high-fidelity and realistic video synthesis. However, current T2V models often struggle to generate physica…

cs.CV2025

ImageGen-CoT: Enhancing Text-to-Image In-context Learning with Chain-of-Thought Reasoning

Jiaqi Liao, Zhengyuan Yang, Linjie Li +4

In this work, we study the problem of Text-to-Image In-Context Learning (T2I-ICL). While Unified Multimodal LLMs (MLLMs) have advanced rapidly in recent years, they struggle with c…

cs.CV2025

LangBridge: Interpreting Image as a Combination of Language Embeddings

Jiaqi Liao, Yuwei Niu, Fanqing Meng +9

Recent years have witnessed remarkable advances in Large Vision-Language Models (LVLMs), which have achieved human-level performance across various complex vision-language tasks. F…

cs.CV2025★ 4 cited

WISE: A World Knowledge-Informed Semantic Evaluation for Text-to-Image Generation

Yuwei Niu, Munan Ning, Mengren Zheng +9

Text-to-Image (T2I) models are capable of generating high-quality artistic creations and visual content. However, existing research and evaluation standards predominantly focus on…

cs.CV2024★ 1 cited

Towards World Simulator: Crafting Physical Commonsense-Based Benchmark for Video Generation

Fanqing Meng, Jiaqi Liao, Xinyu Tan +7

Text-to-video (T2V) models like Sora have made significant strides in visualizing complex prompts, which is increasingly viewed as a promising path towards constructing the univers…

cs.CV2024★ 1 cited

MMIU: Multimodal Multi-image Understanding for Evaluating Large Vision-Language Models

Fanqing Meng, Jin Wang, Chuanhao Li +9

The capability to process multiple images is crucial for Large Vision-Language Models (LVLMs) to develop a more thorough and nuanced understanding of a scene. Recent multi-image LV…