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We propose a zero-shot approach for generating consistent videos of animatedcharacters based on Text-to-Image (T2I) diffusion models. ExistingText-to-Video (T2V) methods are expensive to train and require large-scalevideo datasets to produce diverse characters and motions. At the same time,their zero-shot alternatives fail to produce temporally consistent videos withcontinuous motion. We strive to bridge this gap, and we introduce LatentMan,which leverages existing text-based motion diffusion models to generate diversecontinuous motions to guide the T2I model. To boost the temporal consistency,we introduce the Spatial Latent Alignment module that exploits cross-framedense correspondences that we compute to align the latents of the video frames.Furthermore, we propose Pixel-Wise Guidance to steer the diffusion process in adirection that minimizes visual discrepancies between frames. Our proposedapproach outperforms existing zero-shot T2V approaches in generating videos ofanimated characters in terms of pixel-wise consistency and user preference.Project page https://abdo-eldesokey.github.io/latentman/.

LatentMan: Generating Consistent Animated Characters using Image Diffusion Models