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

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arxiv 2403.08268 v1 pith:FPROL5Q3 submitted 2024-03-13 cs.CV

classification cs.CV
keywords controlmotionmethodsshortanimationfollow-your-clickgenerationmove
verification ladder T0 review T1 audit T2 compute T3 formal
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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 short 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 short motion prompt dataset to improve the short 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. Our framework has simpler yet precise user control and better generation performance than previous methods. Extensive experiments compared with 7 baselines, including both commercial tools and research methods on 8 metrics, suggest the superiority of our approach. Project Page: https://follow-your-click.github.io/

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Hear-Your-Click: Interactive Object-Specific Video-to-Audio Generation

    cs.CV 2025-07 conditional novelty 6.0 of 10

    A new interactive method lets users click on an object in a video and generates audio for just that object, using mask-conditioned contrastive fine-tuning and latent diffusion.

  2. OpenS2V-Nexus: A Detailed Benchmark and Million-Scale Dataset for Subject-to-Video Generation

    cs.CV 2025-05 conditional novelty 6.0 of 10

    A benchmark and five-million-clip dataset for evaluating and training subject-to-video generation models, with three new metrics for subject consistency, naturalness, and text alignment.

  3. Follow-Your-Creation: Empowering 4D Creation through Video Inpainting

    cs.CV 2025-06 conditional novelty 5.0 of 10

    Follow-Your-Creation fine-tunes the Wan2.1 video inpainting model on composite point-cloud and editing masks so a single monocular video can be converted into editable 4D video with new camera motion.

  4. SkipVAR: Accelerating Visual Autoregressive Modeling via Adaptive Frequency-Aware Skipping

    cs.CV 2025-06 conditional novelty 4.0 of 10

    SkipVAR selects, per sample, between step skipping and unconditional branch replacement using handcrafted frequency features and a trained logistic regression, to accelerate visual autoregressive generation.

  5. Controllable and Expressive One-Shot Video Head Swapping

    cs.CV 2025-06

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