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Recent developments in large language models (LLM) and generative AI have unleashed the astonishing capabilities of text-to-image generation systems to synthesize high-quality images that are faithful to a given reference text, known as a…

Human-Computer Interaction · Computer Science 2023-03-17 Yutong Xie , Zhaoying Pan , Jinge Ma , Luo Jie , Qiaozhu Mei

Text-to-video (T2V) generative models have advanced significantly, yet their ability to compose different objects, attributes, actions, and motions into a video remains unexplored. Previous text-to-video benchmarks also neglect this…

Computer Vision and Pattern Recognition · Computer Science 2025-01-16 Kaiyue Sun , Kaiyi Huang , Xian Liu , Yue Wu , Zihan Xu , Zhenguo Li , Xihui Liu

Recent advancements in text-to-video (T2V) diffusion models have significantly enhanced the visual quality of the generated videos. However, even recent T2V models find it challenging to follow text descriptions accurately, especially when…

Computer Vision and Pattern Recognition · Computer Science 2025-04-14 Jialu Li , Shoubin Yu , Han Lin , Jaemin Cho , Jaehong Yoon , Mohit Bansal

This paper offers an insightful examination of how currently top-trending AI technologies, i.e., generative artificial intelligence (Generative AI) and large language models (LLMs), are reshaping the field of video technology, including…

Computer Vision and Pattern Recognition · Computer Science 2024-04-26 Pengyuan Zhou , Lin Wang , Zhi Liu , Yanbin Hao , Pan Hui , Sasu Tarkoma , Jussi Kangasharju

Conditional image-to-video (cI2V) generation aims to synthesize a new plausible video starting from an image (e.g., a person's face) and a condition (e.g., an action class label like smile). The key challenge of the cI2V task lies in the…

Computer Vision and Pattern Recognition · Computer Science 2023-03-27 Haomiao Ni , Changhao Shi , Kai Li , Sharon X. Huang , Martin Renqiang Min

The steady improvements of text-to-image (T2I) generative models lead to slow deprecation of automatic evaluation benchmarks that rely on static datasets, motivating researchers to seek alternative ways to evaluate the T2I progress. In this…

Computer Vision and Pattern Recognition · Computer Science 2025-05-14 Jiahui Chen , Candace Ross , Reyhane Askari-Hemmat , Koustuv Sinha , Melissa Hall , Michal Drozdzal , Adriana Romero-Soriano

In this paper, we address the challenging problem of long-term 3D human motion generation. Specifically, we aim to generate a long sequence of smoothly connected actions from a stream of multiple sentences (i.e., paragraph). Previous…

Computer Vision and Pattern Recognition · Computer Science 2024-06-04 Taeryung Lee , Fabien Baradel , Thomas Lucas , Kyoung Mu Lee , Gregory Rogez

The evolution of Text-to-video (T2V) generative models, trained on large-scale datasets, has been marked by significant progress. However, the sensitivity of T2V generative models to input prompts highlights the critical role of prompt…

Computer Vision and Pattern Recognition · Computer Science 2025-05-07 Bingjie Gao , Xinyu Gao , Xiaoxue Wu , Yujie Zhou , Yu Qiao , Li Niu , Xinyuan Chen , Yaohui Wang

Text-to-video (T2V) generation has recently garnered significant attention thanks to the large multi-modality model Sora. However, T2V generation still faces two important challenges: 1) Lacking a precise open sourced high-quality dataset.…

Computer Vision and Pattern Recognition · Computer Science 2025-02-14 Kepan Nan , Rui Xie , Penghao Zhou , Tiehan Fan , Zhenheng Yang , Zhijie Chen , Xiang Li , Jian Yang , Ying Tai

Recent advances in diffusion models have showcased promising results in the text-to-video (T2V) synthesis task. However, as these T2V models solely employ text as the guidance, they tend to struggle in modeling detailed temporal dynamics.…

Computer Vision and Pattern Recognition · Computer Science 2023-05-24 Seungwoo Lee , Chaerin Kong , Donghyeon Jeon , Nojun Kwak

Recent advancements in video generation, particularly in diffusion models, have driven notable progress in text-to-video (T2V) and image-to-video (I2V) synthesis. However, challenges remain in effectively integrating dynamic motion signals…

Computer Vision and Pattern Recognition · Computer Science 2025-07-04 Ziye Li , Hao Luo , Xincheng Shuai , Henghui Ding

Text-to-video (T2V) models have shown remarkable performance in generating visually reasonable scenes, while their capability to leverage world knowledge for ensuring semantic consistency and factual accuracy remains largely understudied.…

Computer Vision and Pattern Recognition · Computer Science 2025-07-25 Yubin Chen , Xuyang Guo , Zhenmei Shi , Zhao Song , Jiahao Zhang

Inspired by the success of the text-to-image (T2I) generation task, many researchers are devoting themselves to the text-to-video (T2V) generation task. Most of the T2V frameworks usually inherit from the T2I model and add extra-temporal…

Computer Vision and Pattern Recognition · Computer Science 2024-07-24 Yiran Yang , Jinchao Zhang , Ying Deng , Jie Zhou

The generative AI revolution has recently expanded to videos. Nevertheless, current state-of-the-art video models are still lagging behind image models in terms of visual quality and user control over the generated content. In this work, we…

Computer Vision and Pattern Recognition · Computer Science 2023-11-21 Michal Geyer , Omer Bar-Tal , Shai Bagon , Tali Dekel

Recent advancements in text-to-image (T2I) generation using diffusion models have enabled cost-effective video-editing applications by leveraging pre-trained models, eliminating the need for resource-intensive training. However, the…

Computer Vision and Pattern Recognition · Computer Science 2025-06-12 Yangfan He , Sida Li , Jianhui Wang , Kun Li , Xinyuan Song , Xinhang Yuan , Keqin Li , Kuan Lu , Menghao Huo , Jingqun Tang , Yi Xin , Jiaqi Chen , Miao Zhang , Xueqian Wang

Augmenting large language models (LLMs) with user-specific knowledge is crucial for real-world applications, such as personal AI assistants. However, LLMs inherently lack mechanisms for prompt-driven knowledge capture. This paper…

Computation and Language · Computer Science 2024-02-02 Tolga Çöplü , Arto Bendiken , Andrii Skomorokhov , Eduard Bateiko , Stephen Cobb , Joshua J. Bouw

Text-to-video generation is an emerging field in generative AI, enabling the creation of realistic, semantically accurate videos from text prompts. While current models achieve impressive visual quality and alignment with input text, they…

Computer Vision and Pattern Recognition · Computer Science 2025-09-23 Luca Zanchetta , Lorenzo Papa , Luca Maiano , Irene Amerini

Despite recent progress in text-to-image (T2I) generation, existing models often struggle to faithfully capture user intentions from short and under-specified prompts. While prior work has attempted to enhance prompts using large language…

Computer Vision and Pattern Recognition · Computer Science 2025-05-26 Mingrui Wu , Lu Wang , Pu Zhao , Fangkai Yang , Jianjin Zhang , Jianfeng Liu , Yuefeng Zhan , Weihao Han , Hao Sun , Jiayi Ji , Xiaoshuai Sun , Qingwei Lin , Weiwei Deng , Dongmei Zhang , Feng Sun , Qi Zhang , Rongrong Ji

Creating editable videos that depict complex interactions between multiple objects in various artistic styles has long been a challenging task in filmmaking. Progress is often hampered by the scarcity of data sets that contain paired text…

Computer Vision and Pattern Recognition · Computer Science 2024-08-30 Anisha Jain

Creative generation is the synthesis of new, surprising, and valuable samples that reflect user intent yet cannot be envisioned in advance. This task aims to extend human imagination, enabling the discovery of visual concepts that exist in…

Graphics · Computer Science 2025-10-14 Shelly Golan , Yotam Nitzan , Zongze Wu , Or Patashnik