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The performance of computer vision models in certain real-world applications (e.g., rare wildlife observation) is limited by the small number of available images. Expanding datasets using pre-trained generative models is an effective way to…

Computer Vision and Pattern Recognition · Computer Science 2024-12-25 Changjian Chen , Fei Lv , Yalong Guan , Pengcheng Wang , Shengjie Yu , Yifan Zhang , Zhuo Tang

Generating visual instructions in a given context is essential for developing interactive world simulators. While prior works address this problem through either text-guided image manipulation or video prediction, these tasks are typically…

Computer Vision and Pattern Recognition · Computer Science 2025-11-25 Yujiang Pu , Zhanbo Huang , Vishnu Boddeti , Yu Kong

Text-driven video generation has advanced significantly due to developments in diffusion models. Beyond the training and sampling phases, recent studies have investigated noise priors of diffusion models, as improved noise priors yield…

Image and Video Processing · Electrical Eng. & Systems 2025-02-20 Yunlong Yuan , Yuanfan Guo , Chunwei Wang , Wei Zhang , Hang Xu , Li Zhang

Fashion illustration is a crucial medium for designers to convey their creative vision and transform design concepts into tangible representations that showcase the interplay between clothing and the human body. In the context of fashion…

Computer Vision and Pattern Recognition · Computer Science 2024-03-26 Alberto Baldrati , Davide Morelli , Marcella Cornia , Marco Bertini , Rita Cucchiara

We present a video generation model that accurately reproduces object motion, changes in camera viewpoint, and new content that arises over time. Existing video generation methods often fail to produce new content as a function of time…

Computer Vision and Pattern Recognition · Computer Science 2022-06-10 Tim Brooks , Janne Hellsten , Miika Aittala , Ting-Chun Wang , Timo Aila , Jaakko Lehtinen , Ming-Yu Liu , Alexei A. Efros , Tero Karras

Text-to-video diffusion models are notoriously limited in their ability to model temporal aspects such as motion, physics, and dynamic interactions. Existing approaches address this limitation by retraining the model or introducing external…

Computer Vision and Pattern Recognition · Computer Science 2025-06-05 Ariel Shaulov , Itay Hazan , Lior Wolf , Hila Chefer

Text-to-video (T2V) generation has advanced rapidly, yet maintaining consistent character identities across scenes remains a major challenge. Existing personalization methods often focus on facial identity but fail to preserve broader…

Computer Vision and Pattern Recognition · Computer Science 2025-12-09 Ziyang Mai , Yu-Wing Tai

Despite remarkable achievements in video synthesis, achieving granular control over complex dynamics, such as nuanced movement among multiple interacting objects, still presents a significant hurdle for dynamic world modeling, compounded by…

Computer Vision and Pattern Recognition · Computer Science 2024-03-21 Pengxiang Li , Kai Chen , Zhili Liu , Ruiyuan Gao , Lanqing Hong , Guo Zhou , Hua Yao , Dit-Yan Yeung , Huchuan Lu , Xu Jia

Outfit generation is a challenging task in the field of fashion technology, in which the aim is to create a collocated set of fashion items that complement a given set of items. Previous studies in this area have been limited to generating…

Computer Vision and Pattern Recognition · Computer Science 2025-02-04 Dongliang Zhou , Haijun Zhang , Jianghong Ma , Jicong Fan , Zhao Zhang

Recently, diffusion models have shown their impressive ability in visual generation tasks. Besides static images, more and more research attentions have been drawn to the generation of realistic videos. The video generation not only has a…

Computer Vision and Pattern Recognition · Computer Science 2025-11-03 Yucheng Xing , Jinxing Yin , Xiaodong Liu

Text-to-video diffusion models synthesize temporal motion and spatial appearance through iterative denoising, yet how motion is encoded across timesteps remains poorly understood. Practitioners often exploit the empirical heuristic that…

Computer Vision and Pattern Recognition · Computer Science 2025-12-30 Vatsal Baherwani , Yixuan Ren , Abhinav Shrivastava

Diffusion models have achieved impressive performance in video generation, but their iterative denoising process remains computationally expensive due to the large number of tokens processed at each timestep. Recently, progressive…

Computer Vision and Pattern Recognition · Computer Science 2026-05-21 Shikang Zheng , Jingkai Huang , Jiacheng Liu , Guantao Chen , Lixuan , Yuqi Lin , Peiliang Cai , Linfeng Zhang

In this paper, we present VideoGen, a text-to-video generation approach, which can generate a high-definition video with high frame fidelity and strong temporal consistency using reference-guided latent diffusion. We leverage an…

Computer Vision and Pattern Recognition · Computer Science 2023-09-08 Xin Li , Wenqing Chu , Ye Wu , Weihang Yuan , Fanglong Liu , Qi Zhang , Fu Li , Haocheng Feng , Errui Ding , Jingdong Wang

Realistic generative face video synthesis has long been a pursuit in both computer vision and graphics community. However, existing face video generation methods tend to produce low-quality frames with drifted facial identities and…

Computer Vision and Pattern Recognition · Computer Science 2022-08-17 Haonan Qiu , Yuming Jiang , Hang Zhou , Wayne Wu , Ziwei Liu

Diffusion models for garment-centric human generation from text or image prompts have garnered emerging attention for their great application potential. However, existing methods often face a dilemma: lightweight approaches, such as…

Computer Vision and Pattern Recognition · Computer Science 2025-01-22 Ente Lin , Xujie Zhang , Fuwei Zhao , Yuxuan Luo , Xin Dong , Long Zeng , Xiaodan Liang

Generative models that can model and predict sequences of future events can, in principle, learn to capture complex real-world phenomena, such as physical interactions. However, a central challenge in video prediction is that the future is…

Computer Vision and Pattern Recognition · Computer Science 2020-02-13 Manoj Kumar , Mohammad Babaeizadeh , Dumitru Erhan , Chelsea Finn , Sergey Levine , Laurent Dinh , Durk Kingma

Generative models, particularly diffusion models, have made significant success in data synthesis across various modalities, including images, videos, and 3D assets. However, current diffusion models are computationally intensive, often…

Computer Vision and Pattern Recognition · Computer Science 2024-12-10 Yuanzhi Zhu , Hanshu Yan , Huan Yang , Kai Zhang , Junnan Li

The advent of artificial intelligence has contributed in a groundbreaking transformation of the fashion industry, redefining creativity and innovation in unprecedented ways. This work investigates methodologies for generating tailored…

Computer Vision and Pattern Recognition · Computer Science 2024-09-11 Georgia Argyrou , Angeliki Dimitriou , Maria Lymperaiou , Giorgos Filandrianos , Giorgos Stamou

Instructional video generation is an emerging task that aims to synthesize coherent demonstrations of procedural activities from textual descriptions. Such capability has broad implications for content creation, education, and human-AI…

Computer Vision and Pattern Recognition · Computer Science 2025-12-16 Cheeun Hong , German Barquero , Fadime Sener , Markos Georgopoulos , Edgar Schönfeld , Stefan Popov , Yuming Du , Oscar Mañas , Albert Pumarola

The escalating quality of video generated by advanced video generation methods results in new security challenges, while there have been few relevant research efforts: 1) There is no open-source dataset for generated video detection, 2) No…

Computer Vision and Pattern Recognition · Computer Science 2025-04-22 Long Ma , Zhiyuan Yan , Qinglang Guo , Yong Liao , Haiyang Yu , Pengyuan Zhou
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