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Related papers: Coherent Zero-Shot Visual Instruction Generation

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Multistep instructions, such as recipes and how-to guides, greatly benefit from visual aids, such as a series of images that accompany the instruction steps. While Large Language Models (LLMs) have become adept at generating coherent…

Computer Vision and Pattern Recognition · Computer Science 2024-05-17 João Bordalo , Vasco Ramos , Rodrigo Valério , Diogo Glória-Silva , Yonatan Bitton , Michal Yarom , Idan Szpektor , Joao Magalhaes

Text-guided image editing and generation methods have diverse real-world applications. However, text-guided infinite image synthesis faces several challenges. First, there is a lack of text-image paired datasets with high-resolution and…

Computer Vision and Pattern Recognition · Computer Science 2024-12-30 Soyeong Kwon , Taegyeong Lee , Taehwan Kim

The goal of this work is to generate step-by-step visual instructions in the form of a sequence of images, given an input image that provides the scene context and the sequence of textual instructions. This is a challenging problem as it…

Computer Vision and Pattern Recognition · Computer Science 2025-03-26 Tomáš Souček , Prajwal Gatti , Michael Wray , Ivan Laptev , Dima Damen , Josef Sivic

In the text-to-image generation field, recent remarkable progress in Stable Diffusion makes it possible to generate rich kinds of novel photorealistic images. However, current models still face misalignment issues (e.g., problematic spatial…

Computer Vision and Pattern Recognition · Computer Science 2023-08-15 Leigang Qu , Shengqiong Wu , Hao Fei , Liqiang Nie , Tat-Seng Chua

Recent advancements in large scale text-to-image models have opened new possibilities for guiding the creation of images through human-devised natural language. However, while prior literature has primarily focused on the generation of…

Computer Vision and Pattern Recognition · Computer Science 2023-02-09 Hyeonho Jeong , Gihyun Kwon , Jong Chul Ye

The effective communication of procedural knowledge remains a significant challenge in natural language processing (NLP), as purely textual instructions often fail to convey complex physical actions and spatial relationships. We address…

Computation and Language · Computer Science 2025-05-23 Jing Bi , Pinxin Liu , Ali Vosoughi , Jiarui Wu , Jinxi He , Chenliang Xu

Recent advances in diffusion-based text-to-video (T2V) models have demonstrated remarkable progress, but these models still face challenges in generating videos with multiple objects. Most models struggle with accurately capturing complex…

Computer Vision and Pattern Recognition · Computer Science 2025-05-30 Aimon Rahman , Jiang Liu , Ze Wang , Ximeng Sun , Jialian Wu , Xiaodong Yu , Yusheng Su , Vishal M. Patel , Zicheng Liu , Emad Barsoum

Generating a coherent sequence of images that tells a visual story, using text-to-image diffusion models, often faces the critical challenge of maintaining subject consistency across all story scenes. Existing approaches, which typically…

Computer Vision and Pattern Recognition · Computer Science 2025-08-07 Gopalji Gaur , Mohammadreza Zolfaghari , Thomas Brox

With recent advances in Multimodal Large Language Models (MLLMs) showing strong visual understanding and reasoning, interest is growing in using them to improve the editing performance of diffusion models. Despite rapid progress, most…

Computer Vision and Pattern Recognition · Computer Science 2025-10-10 Chong Mou , Qichao Sun , Yanze Wu , Pengze Zhang , Xinghui Li , Fulong Ye , Songtao Zhao , Qian He

The text-to-image synthesis by diffusion models has recently shown remarkable performance in generating high-quality images. Although performs well for simple texts, the models may get confused when faced with complex texts that contain…

Computer Vision and Pattern Recognition · Computer Science 2024-01-15 Chang Yu , Junran Peng , Xiangyu Zhu , Zhaoxiang Zhang , Qi Tian , Zhen Lei

Visual instruction tuning is crucial for enhancing the zero-shot generalization capability of Multi-modal Large Language Models (MLLMs). In this paper, we aim to investigate a fundamental question: ''what makes for good visual…

Computer Vision and Pattern Recognition · Computer Science 2025-02-06 Yifan Du , Hangyu Guo , Kun Zhou , Wayne Xin Zhao , Jinpeng Wang , Chuyuan Wang , Mingchen Cai , Ruihua Song , Ji-Rong Wen

Although powerful for image generation, consistent and controllable video is a longstanding problem for diffusion models. Video models require extensive training and computational resources, leading to high costs and large environmental…

Computer Vision and Pattern Recognition · Computer Science 2024-10-10 Muhammad Haaris Khan , Hadrien Reynaud , Bernhard Kainz

We introduce the new task of generating Illustrated Instructions, i.e., visual instructions customized to a user's needs. We identify desiderata unique to this task, and formalize it through a suite of automatic and human evaluation…

Computer Vision and Pattern Recognition · Computer Science 2024-04-16 Sachit Menon , Ishan Misra , Rohit Girdhar

Recent text-to-video generation approaches rely on computationally heavy training and require large-scale video datasets. In this paper, we introduce a new task of zero-shot text-to-video generation and propose a low-cost approach (without…

Computer Vision and Pattern Recognition · Computer Science 2023-03-24 Levon Khachatryan , Andranik Movsisyan , Vahram Tadevosyan , Roberto Henschel , Zhangyang Wang , Shant Navasardyan , Humphrey Shi

Zero-shot, training-free, image-based text-to-video generation is an emerging area that aims to generate videos using existing image-based diffusion models. Current methods in this space require specific architectural changes to image…

Computer Vision and Pattern Recognition · Computer Science 2025-04-10 Diljeet Jagpal , Xi Chen , Vinay P. Namboodiri

Recent endeavors in Multimodal Large Language Models (MLLMs) aim to unify visual comprehension and generation by combining LLM and diffusion models, the state-of-the-art in each task, respectively. Existing approaches rely on spatial visual…

Computer Vision and Pattern Recognition · Computer Science 2025-04-22 Kaihang Pan , Wang Lin , Zhongqi Yue , Tenglong Ao , Liyu Jia , Wei Zhao , Juncheng Li , Siliang Tang , Hanwang Zhang

Large-scale generative models, such as text-to-image diffusion models, have garnered widespread attention across diverse domains due to their creative and high-fidelity image generation. Nonetheless, existing large-scale diffusion models…

Computer Vision and Pattern Recognition · Computer Science 2024-08-28 Younghyun Kim , Geunmin Hwang , Junyu Zhang , Eunbyung Park

Consistency models have demonstrated powerful capability in efficient image generation and allowed synthesis within a few sampling steps, alleviating the high computational cost in diffusion models. However, the consistency model in the…

Computer Vision and Pattern Recognition · Computer Science 2023-12-15 Xiang Wang , Shiwei Zhang , Han Zhang , Yu Liu , Yingya Zhang , Changxin Gao , Nong Sang

Recent advancements in text-to-image models, particularly diffusion models, have shown significant promise. However, compositional text-to-image models frequently encounter difficulties in generating high-quality images that accurately…

Computer Vision and Pattern Recognition · Computer Science 2023-10-11 Song Wen , Guian Fang , Renrui Zhang , Peng Gao , Hao Dong , Dimitris Metaxas

Diffusion models have achieved impressive results in generative tasks for text-to-video (T2V) synthesis. However, achieving accurate text alignment in T2V generation remains challenging due to the complex temporal dependencies across…

Computer Vision and Pattern Recognition · Computer Science 2025-10-21 Jaemin Kim , Bryan Sangwoo Kim , Jong Chul Ye
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