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We propose a zero-shot approach for consistent Text-to-Animated-Characterssynthesis based on pre-trained 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. Westrive to bridge this gap, and we introduce a zero-shot approach that producestemporally consistent videos of animated characters and requires no training orfine-tuning. We leverage existing text-based motion diffusion models togenerate diverse motions that we utilize to guide a T2I model. To achievetemporal consistency, we introduce the Spatial Latent Alignment module thatexploits cross-frame dense correspondences that we compute to align the latentsof the video frames. Furthermore, we propose Pixel-Wise Guidance to steer thediffusion process in a direction that minimizes visual discrepancies. Ourproposed approach generates temporally consistent videos with diverse motionsand styles, outperforming existing zero-shot T2V approaches in terms ofpixel-wise consistency and user preference.

Text2AC-Zero: Consistent Synthesis of Animated Characters using 2D Diffusion