LatentMan: Generating Consistent Animated Characters using Image Diffusion Models

• URL: http://arxiv.org/abs/2312.07133v2
• Date: Sun, 2 Jun 2024 10:50:54 GMT
• Title: LatentMan: Generating Consistent Animated Characters using Image Diffusion Models
• Authors: Abdelrahman Eldesokey, Peter Wonka,
• Abstract summary: We propose a zero-shot approach for generating consistent videos of animated characters based on Text-to-Image (T2I) diffusion models. Our proposed approach outperforms existing zero-shot T2V approaches in generating videos of animated characters in terms of pixel-wise consistency and user preference.
• Score: 44.18315132571804
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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/.

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