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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2408.16704 |
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| _version_ | 1866912006667239424 |
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| author | Jain, Anisha |
| author_facet | Jain, Anisha |
| contents | 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 descriptions and corresponding videos that showcase these interactions. This paper introduces a novel depth-conditioning approach that significantly advances this field by enabling the generation of coherent and diverse videos from just a single text-video pair using a pre-trained depth-aware Text-to-Image (T2I) model. Our method fine-tunes the pre-trained model to capture continuous motion by employing custom-designed spatial and temporal attention mechanisms. During inference, we use the DDIM inversion to provide structural guidance for video generation. This innovative technique allows for continuously controllable depth in videos, facilitating the generation of multiobject interactions while maintaining the concept generation and compositional strengths of the original T2I model across various artistic styles, such as photorealism, animation, and impressionism. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_16704 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | One-Shot Learning Meets Depth Diffusion in Multi-Object Videos Jain, Anisha Computer Vision and Pattern Recognition 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 descriptions and corresponding videos that showcase these interactions. This paper introduces a novel depth-conditioning approach that significantly advances this field by enabling the generation of coherent and diverse videos from just a single text-video pair using a pre-trained depth-aware Text-to-Image (T2I) model. Our method fine-tunes the pre-trained model to capture continuous motion by employing custom-designed spatial and temporal attention mechanisms. During inference, we use the DDIM inversion to provide structural guidance for video generation. This innovative technique allows for continuously controllable depth in videos, facilitating the generation of multiobject interactions while maintaining the concept generation and compositional strengths of the original T2I model across various artistic styles, such as photorealism, animation, and impressionism. |
| title | One-Shot Learning Meets Depth Diffusion in Multi-Object Videos |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.16704 |