2 citations · 2 across the 13 of their papers we have counts for
14 papers
Refinement via Regeneration: Enlarging Modification Space Boosts Image Refinement in Unified Multimodal Models
Jiayi Guo, Linqing Wang, Jiangshan Wang +6
Unified multimodal models (UMMs) integrate visual understanding and generation within a single framework. For text-to-image (T2I) tasks, this unified capability allows UMMs to refi…
Meta-CoT: Enhancing Granularity and Generalization in Image Editing
Shiyi Zhang, Yiji Cheng, Tiankai Hang +8
Unified multi-modal understanding/generative models have shown improved image editing performance by incorporating fine-grained understanding into their Chain-of-Thought (CoT) proc…
SOAR: Self-Correction for Optimal Alignment and Refinement in Diffusion Models
You Qin, Linqing Wang, Hao Fei +4
The post-training pipeline for diffusion models currently has two stages: supervised fine-tuning (SFT) on curated data and reinforcement learning (RL) with reward models. A fundame…
Hierarchical SVG Tokenization: Learning Compact Visual Programs for Scalable Vector Graphics Modeling
Ximing Xing, Ziteng Xue, Zhenxi Li +8
Recent large language models have shifted SVG generation from differentiable rendering optimization to autoregressive program synthesis. However, existing approaches still rely on…
Generative Visual Chain-of-Thought for Image Editing
Zijin Yin, Tiankai Hang, Yiji Cheng +9
Existing image editing methods struggle to perceive where to edit, especially under complex scenes and nuanced spatial instructions. To address this issue, we propose Generative Vi…
ChatUMM: Robust Context Tracking for Conversational Interleaved Generation
Wenxun Dai, Zhiyuan Zhao, Yule Zhong +12
Unified multimodal models (UMMs) have achieved remarkable progress yet remain constrained by a single-turn interaction paradigm, effectively functioning as solvers for independent…
Re-Align: Structured Reasoning-guided Alignment for In-Context Image Generation and Editing
Runze He, Yiji Cheng, Tiankai Hang +11
In-context image generation and editing (ICGE) enables users to specify visual concepts through interleaved image-text prompts, demanding precise understanding and faithful executi…
TAG-MoE: Task-Aware Gating for Unified Generative Mixture-of-Experts
Yu Xu, Hongbin Yan, Juan Cao +11
Unified image generation and editing models suffer from severe task interference in dense diffusion transformers architectures, where a shared parameter space must compromise betwe…
JarvisEvo: Towards a Self-Evolving Photo Editing Agent with Synergistic Editor-Evaluator Optimization
Yunlong Lin, Linqing Wang, Kunjie Lin +11
Agent-based editing models have substantially advanced interactive experiences, processing quality, and creative flexibility. However, two critical challenges persist: (1) instruct…
UniGenBench++: A Unified Semantic Evaluation Benchmark for Text-to-Image Generation
Yibin Wang, Zhimin Li, Yuhang Zang +8
Recent progress in text-to-image (T2I) generation underscores the importance of reliable benchmarks in evaluating how accurately generated images reflect the semantics of their tex…
PromptEnhancer: A Simple Approach to Enhance Text-to-Image Models via Chain-of-Thought Prompt Rewriting
Linqing Wang, Ximing Xing, Yiji Cheng +13
Recent advancements in text-to-image (T2I) diffusion models have demonstrated remarkable capabilities in generating high-fidelity images. However, these models often struggle to fa…
Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference
Xiangwei Shen, Zhimin Li, Zhantao Yang +6
Recent studies have demonstrated the effectiveness of directly aligning diffusion models with human preferences using differentiable reward. However, they exhibit two primary chall…
Pref-GRPO: Pairwise Preference Reward-based GRPO for Stable Text-to-Image Reinforcement Learning
Yibin Wang, Zhimin Li, Yuhang Zang +6
Recent advancements highlight the importance of GRPO-based reinforcement learning methods and benchmarking in enhancing text-to-image (T2I) generation. However, current methods usi…
Unified Multimodal Chain-of-Thought Reward Model through Reinforcement Fine-Tuning
Yibin Wang, Zhimin Li, Yuhang Zang +4
Recent advances in multimodal Reward Models (RMs) have shown significant promise in delivering reward signals to align vision models with human preferences. However, current RMs ar…