Sketching the Future (STF): Applying Conditional Control Techniques to
  Text-to-Video Models
Rohan Dhesikan Vignesh Rajmohan
The proliferation of video content demands efficient and flexible neural
network based approaches for generating new video content. In this paper, we
propose a novel approach that combines zero-shot text-to-video generation with
ControlNet to improve the output of these models. Our method takes multiple
sketched frames as input and generates video output that matches the flow of
these frames, building upon the Text-to-Video Zero architecture and
incorporating ControlNet to enable additional input conditions. By first
interpolating frames between the inputted sketches and then running
Text-to-Video Zero using the new interpolated frames video as the control
technique, we leverage the benefits of both zero-shot text-to-video generation
and the robust control provided by ControlNet. Experiments demonstrate that our
method excels at producing high-quality and remarkably consistent video content
that more accurately aligns with the user's intended motion for the subject
within the video. We provide a comprehensive resource package, including a demo
video, project website, open-source GitHub repository, and a Colab playground
to foster further research and application of our proposed method.