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We present an efficient text-to-video generation framework based on latent diffusion models, termed MagicVideo. MagicVideo can generate smooth video clips that are concordant with the given text descriptions. Due to a novel and efficient 3D…

Computer Vision and Pattern Recognition · Computer Science 2023-05-12 Daquan Zhou , Weimin Wang , Hanshu Yan , Weiwei Lv , Yizhe Zhu , Jiashi Feng

Text-guided diffusion models have shown superior performance in image/video generation and editing. While few explorations have been performed in 3D scenarios. In this paper, we discuss three fundamental and interesting problems on this…

Computer Vision and Pattern Recognition · Computer Science 2023-10-13 Gang Li , Heliang Zheng , Chaoyue Wang , Chang Li , Changwen Zheng , Dacheng Tao

Text-to-image (T2I) diffusion models, with their impressive generative capabilities, have been adopted for image editing tasks, demonstrating remarkable efficacy. However, due to attention leakage and collision between the cross-attention…

Computer Vision and Pattern Recognition · Computer Science 2025-05-01 Xingxi Yin , Zhi Li , Jingfeng Zhang , Chenglin Li , Yin Zhang

Recent advances in image editing with diffusion models have achieved impressive results, offering fine-grained control over the generation process. However, these methods are computationally intensive because of their iterative nature.…

Computer Vision and Pattern Recognition · Computer Science 2025-06-25 Ilia Beletskii , Andrey Kuznetsov , Aibek Alanov

Our work addresses limitations seen in previous approaches for object-centric editing problems, such as unrealistic results due to shape discrepancies and limited control in object replacement or insertion. To this end, we introduce…

Computer Vision and Pattern Recognition · Computer Science 2024-12-23 Trong-Tung Nguyen , Duc-Anh Nguyen , Anh Tran , Cuong Pham

ControlNet offers a powerful way to guide diffusion-based generative models, yet most implementations rely on ad-hoc heuristics to choose which network blocks to control-an approach that varies unpredictably with different tasks. To address…

Machine Learning · Computer Science 2025-02-21 Zheng Fang , Lichuan Xiang , Xu Cai , Kaicheng Zhou , Hongkai Wen

Instructional video editing applies edits to an input video using only text prompts, enabling intuitive natural-language control. Despite rapid progress, most methods still require fixed-length inputs and substantial compute. Meanwhile,…

Computer Vision and Pattern Recognition · Computer Science 2026-03-03 Mohammadreza Salehi , Mehdi Noroozi , Luca Morreale , Ruchika Chavhan , Malcolm Chadwick , Alberto Gil Ramos , Abhinav Mehrotra

Traditional 3D content creation tools empower users to bring their imagination to life by giving them direct control over a scene's geometry, appearance, motion, and camera path. Creating computer-generated videos, however, is a tedious…

Computer Vision and Pattern Recognition · Computer Science 2023-12-05 Shengqu Cai , Duygu Ceylan , Matheus Gadelha , Chun-Hao Paul Huang , Tuanfeng Yang Wang , Gordon Wetzstein

Sketch-guided image editing aims to achieve local fine-tuning of the image based on the sketch information provided by the user, while maintaining the original status of the unedited areas. Due to the high cost of acquiring human sketches,…

Computer Vision and Pattern Recognition · Computer Science 2023-11-15 Weihang Mao , Bo Han , Zihao Wang

Diffusion Transformers (DiTs) have demonstrated remarkable scalability and quality in image and video generation, prompting growing interest in extending them to controllable generation and editing tasks. However, compared to the image…

Computer Vision and Pattern Recognition · Computer Science 2026-03-17 Ruonan Yu , Zhenxiong Tan , Zigeng Chen , Songhua Liu , Xinchao Wang

Diffusion-based image editing models have achieved significant progress in real world applications. However, conventional models typically rely on natural language prompts, which often lack the precision required to localize target objects.…

Computer Vision and Pattern Recognition · Computer Science 2026-04-14 Haohang Xu , Lin Liu , Zhibo Zhang , Rong Cong , Xiaopeng Zhang , Qi Tian

Recent advances in generative AI have significantly enhanced image and video editing, particularly in the context of text prompt control. State-of-the-art approaches predominantly rely on diffusion models to accomplish these tasks. However,…

Computer Vision and Pattern Recognition · Computer Science 2024-04-03 Haoyu Ma , Shahin Mahdizadehaghdam , Bichen Wu , Zhipeng Fan , Yuchao Gu , Wenliang Zhao , Lior Shapira , Xiaohui Xie

Video diffusion models have recently made great progress in generation quality, but are still limited by the high memory and computational requirements. This is because current video diffusion models often attempt to process…

Computer Vision and Pattern Recognition · Computer Science 2024-03-22 Sihyun Yu , Weili Nie , De-An Huang , Boyi Li , Jinwoo Shin , Anima Anandkumar

Image generation has recently seen tremendous advances, with diffusion models allowing to synthesize convincing images for a large variety of text prompts. In this article, we propose DiffEdit, a method to take advantage of text-conditioned…

Computer Vision and Pattern Recognition · Computer Science 2022-10-21 Guillaume Couairon , Jakob Verbeek , Holger Schwenk , Matthieu Cord

Text-based talking-head video editing aims to efficiently insert, delete, and substitute segments of talking videos through a user-friendly text editing approach. It is challenging because of \textbf{1)} generalizable talking-face…

Computer Vision and Pattern Recognition · Computer Science 2024-07-23 Bo Han , Heqing Zou , Haoyang Li , Guangcong Wang , Chng Eng Siong

Instruction-based video editing is a natural way to control video content with text, but adapting a video generation model into an editor usually appears data-hungry. At the same time, high-quality video editing data remains scarce. In this…

Computer Vision and Pattern Recognition · Computer Science 2026-04-13 Zhefan Rao , Bin Zou , Haoxuan Che , Xuanhua He , Chong Hou Choi , Yanheng Li , Rui Liu , Qifeng Chen

This paper introduces a generative model designed for multimodal control over text-to-image foundation generative AI models such as Stable Diffusion, specifically tailored for engineering design synthesis. Our model proposes parametric,…

Artificial Intelligence · Computer Science 2024-12-09 Rui Zhou , Yanxia Zhang , Chenyang Yuan , Frank Permenter , Nikos Arechiga , Matt Klenk , Faez Ahmed

Fashion image editing is a crucial tool for designers to convey their creative ideas by visualizing design concepts interactively. Current fashion image editing techniques, though advanced with multimodal prompts and powerful diffusion…

Computer Vision and Pattern Recognition · Computer Science 2025-09-18 Xiaolong Wang , Zhi-Qi Cheng , Jue Wang , Xiaojiang Peng

Facial video editing has become increasingly important for content creators, enabling the manipulation of facial expressions and attributes. However, existing models encounter challenges such as poor editing quality, high computational…

Computer Vision and Pattern Recognition · Computer Science 2025-01-14 Tharun Anand , Aryan Garg , Kaushik Mitra

Despite recent advances in UNet-based image editing, methods for shape-aware object editing in high-resolution images are still lacking. Compared to UNet, Diffusion Transformers (DiT) demonstrate superior capabilities to effectively capture…

Computer Vision and Pattern Recognition · Computer Science 2024-11-08 Kunyu Feng , Yue Ma , Bingyuan Wang , Chenyang Qi , Haozhe Chen , Qifeng Chen , Zeyu Wang