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The proliferation of video content demands efficient and flexible neuralnetwork based approaches for generating new video content. In this paper, wepropose a novel approach that combines zero-shot text-to-video generation withControlNet to improve the output of these models. Our method takes multiplesketched frames as input and generates video output that matches the flow ofthese frames, building upon the Text-to-Video Zero architecture andincorporating ControlNet to enable additional input conditions. By firstinterpolating frames between the inputted sketches and then runningText-to-Video Zero using the new interpolated frames video as the controltechnique, we leverage the benefits of both zero-shot text-to-video generationand the robust control provided by ControlNet. Experiments demonstrate that ourmethod excels at producing high-quality and remarkably consistent video contentthat more accurately aligns with the user's intended motion for the subjectwithin the video. We provide a comprehensive resource package, including a demovideo, project website, open-source GitHub repository, and a Colab playgroundto foster further research and application of our proposed method.

Sketching the Future (STF): Applying Conditional Control Techniques to Text-to-Video Models