LatentMan: Generating Consistent Animated Characters using Image Diffusion Models
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arXiv
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| Format: | Preprint |
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2023
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| _version_ | 1866929368735940608 |
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| author | Eldesokey, Abdelrahman Wonka, Peter |
| author_facet | Eldesokey, Abdelrahman Wonka, Peter |
| contents | We propose a zero-shot approach for generating consistent videos of animated characters based on Text-to-Image (T2I) diffusion models. Existing Text-to-Video (T2V) methods are expensive to train and require large-scale video datasets to produce diverse characters and motions. At the same time, their zero-shot alternatives fail to produce temporally consistent videos with continuous motion. We strive to bridge this gap, and we introduce LatentMan, which leverages existing text-based motion diffusion models to generate diverse continuous motions to guide the T2I model. To boost the temporal consistency, we introduce the Spatial Latent Alignment module that exploits cross-frame dense correspondences that we compute to align the latents of the video frames. Furthermore, we propose Pixel-Wise Guidance to steer the diffusion process in a direction that minimizes visual discrepancies between frames. Our proposed approach outperforms existing zero-shot T2V approaches in generating videos of animated characters in terms of pixel-wise consistency and user preference. Project page https://abdo-eldesokey.github.io/latentman/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_07133 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | LatentMan: Generating Consistent Animated Characters using Image Diffusion Models Eldesokey, Abdelrahman Wonka, Peter Computer Vision and Pattern Recognition Machine Learning We propose a zero-shot approach for generating consistent videos of animated characters based on Text-to-Image (T2I) diffusion models. Existing Text-to-Video (T2V) methods are expensive to train and require large-scale video datasets to produce diverse characters and motions. At the same time, their zero-shot alternatives fail to produce temporally consistent videos with continuous motion. We strive to bridge this gap, and we introduce LatentMan, which leverages existing text-based motion diffusion models to generate diverse continuous motions to guide the T2I model. To boost the temporal consistency, we introduce the Spatial Latent Alignment module that exploits cross-frame dense correspondences that we compute to align the latents of the video frames. Furthermore, we propose Pixel-Wise Guidance to steer the diffusion process in a direction that minimizes visual discrepancies between frames. Our proposed approach outperforms existing zero-shot T2V approaches in generating videos of animated characters in terms of pixel-wise consistency and user preference. Project page https://abdo-eldesokey.github.io/latentman/. |
| title | LatentMan: Generating Consistent Animated Characters using Image Diffusion Models |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2312.07133 |