arXiv AI

From Prompting to Composing: A Spatial Canvas Interface for Poster Generation

arXiv AI

Jun 17

Detail++: Training-Free Detail Enhancer for Text-to-Image Diffusion Models

arXiv:2507. 17853v2 Announce Type: replace-cross Abstract: Recent advances in text-to-image (T2I) generation have led to impressive visual results.

By Lifeng Chen, Jiner Wang, Zihao Pan, Beier Zhu, Xiaofeng Yang, Chi Zhang

diffusion benchmarks safety

arXiv Computer Vision

Aug 21

PosterText: Towards Unified Visual Text Generation and Editing for E-commerce Poster

arXiv:2608. 16289v3 Announce Type: replace Abstract: Automated e-commerce poster design requires both high-quality poster generation and flexible editing of existing designs.

By Xiaoan Liu, Lichen Ma, Zipeng Guo, Yu He, Xiaoyan Su, Shaojie Guo, Jingling Fu, Xiaolong Fu, Hao Yang, Tongxuan Liu, Yu Guo, Fei Wang, Xinyi Liu, Yongjun Zhang, Junshi Huang

reinforcement-learning efficiency benchmarks safety

arXiv AI

Jul 8

CanvasAgent: Enabling Complex Image Creation and Editing via Visual Tool Orchestration

arXiv:2607. 05465v1 Announce Type: cross Abstract: Complex image creation and editing often require more than a single generation or editing model.

By Hairui Zhu, Yiying Yang, Tengjin Weng, Ziyu Lu, Xiao Yao, Xiaoyang Ye, Lin Ma, Wenhao Jiang

agents reinforcement-learning robotics fine-tuning multimodal

Hugging Face Trending Papers

Jun 3

MetaPoint: Unlocking Precise Spatial Control in Agentic Visual Generation

Generative visual models fundamentally struggle with precise spatial control. This arises from a core disconnect: models can process textual descriptions of space but cannot directly map numerical coordinates onto the 2D image canvas.

agents

arXiv AI

Aug 7

CoCo: Code as CoT for Text-to-Image Preview and Rare Concept Generation

arXiv:2603. 08652v2 Announce Type: replace Abstract: Recent advancements in Unified Multimodal Models (UMMs) have significantly advanced text-to-image (T2I) generation, particularly through the integration of Chain-of-Thought (CoT) reasoning.

By Haodong Li, Chunmei Qing, Huanyu Zhang, Dongzhi Jiang, Yihang Zou, Hongbo Peng, Dingming Li, Yuhong Dai, ZePeng Lin, Juanxi Tian, Yi Zhou, Siqi Dai, Jingwei Wu, Pheng-Ann Heng

diffusion multimodal

arXiv AI

Jun 9

Seeing is Believing: Aligning Prompt Rewriting with Visual Anchors for Text-to-Image Generation

arXiv:2606. 08492v1 Announce Type: cross Abstract: Despite the impressive capabilities of text-to-image (T2I) models, an intent-generation gap often persists due to the brevity and ambiguity of user prompts.

By Xuanyi Liu, Deyi Ji, Junyu Lu, Jing Wang, Qianxiong Xu, Xuhang Chen, Tianrun Chen, Siwei Ma

llms diffusion multimodal

arXiv Computation and Language

Sep 30

POET: Preference Optimization for Enhanced Text-to-Image Generation

arXiv:2510.12041v3 Announce Type: replace Abstract: Recent advances in text-to-image (T2I) generation have achieved impressive results, yet existing models often struggle with simple or underspecifie...

By Ruibo Chen, Jiacheng Pan, Heng Huang, Zhenheng Yang

llms diffusion reinforcement-learning fine-tuning multimodal benchmarks safety

arXiv AI

Jun 2

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts?

arXiv:2505. 16915v3 Announce Type: replace-cross Abstract: While recent Text-to-Image (T2I) models show impressive capabilities in synthesizing images from brief descriptions, they struggle with the long, detailed prompts required for professional applications.

By Qirui Jiao, Daoyuan Chen, Yilun Huang, Xika Lin, Ying Shen, Yaliang Li

diffusion benchmarks

arXiv AI

1d ago

VibeEdit: Image Editing with Canvas Instructions

arXiv:2610.12229v1 Announce Type: cross Abstract: In text-guided image editing, describing the desired change is often straightforward, but identifying the intended object or region can be cumbersome...

By Jinjing Zhao, Fangyun Wei, Yitong Wang, Xiuyu Wu, Yunuo Chen, Yang Yue, Sirui Zhang, Wenbo Wang, Hongyang Zhang, Dong Chen, Yan Lu, Chang Xu

reinforcement-learning fine-tuning multimodal benchmarks

arXiv AI

Sep 17

CompArt: Operationalizing Aesthetic Alignment in Text-to-Image Generation via Principles of Art

CompArt introduces a new approach to aesthetic alignment in text-to-image generation by using the Principles of Art (PoA) such as Balance, Rhythm, and Emphasis to define explicit compositional constraints. The authors create a large dataset of 80,032 WikiArt images, each annotated with PoA analyses generated by a multimodal LLM, and present ArtDapter, a lightweight adapter that steers a pretrained diffusion model along ten PoA dimensions while preserving semantic fidelity. Experiments demonstrate that CompArt outperforms strong baselines in adhering to PoA controls under a dual evaluation protocol.

By Zhe Jin, Tat-Seng Chua

llms diffusion multimodal safety

arXiv AI

Jun 24

IV-CoT: Implicit Visual Chain-of-Thought for Structure-Aware Text-to-Image Generation

arXiv:2606. 24849v1 Announce Type: cross Abstract: Unified multi-modal large language models (MLLMs) have achieved strong text-to-image generation quality, but still struggle with structure-aware prompt following, where object counts, spatial relations, attribute bindings, and coarse layouts must be preserved.

By Zixuan Li, Haokun Lin, Yicheng Xiao, Zhiwei Li, Xinyang Song, Zelong Zheng, Yong He, Heng Yao, Ke Ding, Chao Yu, Chuan Yuan, Qi Li, Zhenan Sun

llms diffusion multimodal

arXiv AI

Jun 10

Scone: Bridging Composition and Distinction in Subject-Driven Image Generation via Unified Understanding-Generation Modeling

arXiv:2512. 12675v3 Announce Type: replace-cross Abstract: Subject-driven image generation has advanced from single- to multi-subject composition, while neglecting distinction, the ability to distinguish and generate the correct subject when inputs contain multiple candidates.

By Yuran Wang, Bohan Zeng, Chengzhuo Tong, Wenxuan Liu, Yang Shi, Xiaochen Ma, Hao Liang, Yuanxing Zhang, Wentao Zhang

diffusion benchmarks safety

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