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We propose Make-A-Video -- an approach for directly translating thetremendous recent progress in Text-to-Image (T2I) generation to Text-to-Video(T2V). Our intuition is simple: learn what the world looks like and how it isdescribed from paired text-image data, and learn how the world moves fromunsupervised video footage. Make-A-Video has three advantages: (1) itaccelerates training of the T2V model (it does not need to learn visual andmultimodal representations from scratch), (2) it does not require pairedtext-video data, and (3) the generated videos inherit the vastness (diversityin aesthetic, fantastical depictions, etc.) of today's image generation models.We design a simple yet effective way to build on T2I models with novel andeffective spatial-temporal modules. First, we decompose the full temporal U-Netand attention tensors and approximate them in space and time. Second, we designa spatial temporal pipeline to generate high resolution and frame rate videoswith a video decoder, interpolation model and two super resolution models thatcan enable various applications besides T2V. In all aspects, spatial andtemporal resolution, faithfulness to text, and quality, Make-A-Video sets thenew state-of-the-art in text-to-video generation, as determined by bothqualitative and quantitative measures.

Make-A-Video: Text-to-Video Generation without Text-Video Data