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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.00148 |
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| _version_ | 1866912138759503872 |
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| author | Pandey, Karran Gadelha, Matheus Hold-Geoffroy, Yannick Singh, Karan Mitra, Niloy J. Guerrero, Paul |
| author_facet | Pandey, Karran Gadelha, Matheus Hold-Geoffroy, Yannick Singh, Karan Mitra, Niloy J. Guerrero, Paul |
| contents | Predicting diverse object motions from a single static image remains challenging, as current video generation models often entangle object movement with camera motion and other scene changes. While recent methods can predict specific motions from motion arrow input, they rely on synthetic data and predefined motions, limiting their application to complex scenes. We introduce Motion Modes, a training-free approach that explores a pre-trained image-to-video generator's latent distribution to discover various distinct and plausible motions focused on selected objects in static images. We achieve this by employing a flow generator guided by energy functions designed to disentangle object and camera motion. Additionally, we use an energy inspired by particle guidance to diversify the generated motions, without requiring explicit training data. Experimental results demonstrate that Motion Modes generates realistic and varied object animations, surpassing previous methods and even human predictions regarding plausibility and diversity. Project Webpage: https://motionmodes.github.io/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_00148 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Motion Modes: What Could Happen Next? Pandey, Karran Gadelha, Matheus Hold-Geoffroy, Yannick Singh, Karan Mitra, Niloy J. Guerrero, Paul Computer Vision and Pattern Recognition Predicting diverse object motions from a single static image remains challenging, as current video generation models often entangle object movement with camera motion and other scene changes. While recent methods can predict specific motions from motion arrow input, they rely on synthetic data and predefined motions, limiting their application to complex scenes. We introduce Motion Modes, a training-free approach that explores a pre-trained image-to-video generator's latent distribution to discover various distinct and plausible motions focused on selected objects in static images. We achieve this by employing a flow generator guided by energy functions designed to disentangle object and camera motion. Additionally, we use an energy inspired by particle guidance to diversify the generated motions, without requiring explicit training data. Experimental results demonstrate that Motion Modes generates realistic and varied object animations, surpassing previous methods and even human predictions regarding plausibility and diversity. Project Webpage: https://motionmodes.github.io/ |
| title | Motion Modes: What Could Happen Next? |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.00148 |