Follow-Your-Click: Open-domain Regional Image Animation via Motion Prompts

Despite recent advances in image-to-video generation, better controllability and local animation are less explored. Most existing image-to-video methods are not locally aware and tend to move the entire scene. However, human artists may need to control the movement of different objects or regions. Additionally, current I2V methods require users not only to describe the target motion but also to provide redundant detailed descriptions of frame contents.These two issues hinder the practical utilization of current I2V tools. In this paper, we propose a practical framework, named Follow-Your-Click, to achieve image animation with a simple user click (for specifying what to move) and a motion prompt (for specifying how to move). Technically, we propose the first-frame masking strategy, which significantly improves the video generation quality, and a motion-augmented module equipped with a motion prompt dataset to improve the motion prompt following abilities of our model. To further control the motion speed, we propose flow-based motion magnitude control to control the speed of target movement more precisely. Extensive experiments compared with 7 baselines, including both commercial tools and research methods on 8 metrics, suggest the superiority of our approach.

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Publication Details

Journal
Proceedings of the AAAI Conference on Artificial Intelligence
Published
2025-04-11
DOI
https://doi.org/10.1609/aaai.v39i6.32643
Citations
5
Primary Topic
Video Analysis and Summarization
Type
article
Field-Weighted Citation Impact
2.16
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Follow-Your-Click: Open-domain Regional Image Animation via Motion Prompts

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5 citations
Proceedings of the AAAI Conference on Artificial Intelligence
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article

Follow-Your-Click: Open-domain Regional Image Animation via Motion Prompts

Leqi Shen, Chenyang Qi, Andong Wang, Chengfei Cai, Heung‐Yeung Shum, Hongfa Wang, Yue Ma, Zhifeng Li, Qifeng Chen, Wei Liu, Yingqing He, J. Ying
article en
5 citations

Abstract

Despite recent advances in image-to-video generation, better controllability and local animation are less explored. Most existing image-to-video methods are not locally aware and tend to move the entire scene. However, human artists may need to control the movement of different objects or regions. Additionally, current I2V methods require users not only to describe the target motion but also to provide redundant detailed descriptions of frame contents.These two issues hinder the practical utilization of current I2V tools. In this paper, we propose a practical framework, named Follow-Your-Click, to achieve image animation with a simple user click (for specifying what to move) and a motion prompt (for specifying how to move). Technically, we propose the first-frame masking strategy, which significantly improves the video generation quality, and a motion-augmented module equipped with a motion prompt dataset to improve the motion prompt following abilities of our model. To further control the motion speed, we propose flow-based motion magnitude control to control the speed of target movement more precisely. Extensive experiments compared with 7 baselines, including both commercial tools and research methods on 8 metrics, suggest the superiority of our approach.

Proceedings of the AAAI Conference on Artificial IntelligenceVol. 39(6)
Hong Kong University of Science and Technology (HK), Tencent (China) (CN), University of Hong Kong (HK), Tsinghua University (CN)
Openalex Percentile: Top 11%
Video Analysis and Summarization
2.16
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