Long Video Generation with Time-Agnostic VQGAN and Time-Sensitive Transformer
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Cited by 107 in Diffusion models, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
Videos are created to express emotion, exchange information, and share experiences. Video synthesis has intrigued researchers for a long time. Despite the rapid progress driven by advances in visual synthesis, most existing studies focus on improving the frames' quality and the transitions between them, while little progress has been made in generating longer videos. In this paper, we present a method that builds on 3D-VQGAN and transformers to generate videos with thousands of frames. Our evaluation shows that our model trained on 16-frame video clips from standard benchmarks such as UCF-101, Sky Time-lapse, and Taichi-HD datasets can generate diverse, coherent, and high-quality long videos. We also showcase conditional extensions of our approach for generating meaningful long videos by incorporating temporal information with text and audio. Videos and code can be found at https://songweige.github.io/projects/tats/index.html.
Key facts. Cited by 244 papers in the map (data through 14 Oct 2026). Builds on 71 of its 87 references that are in the map. First posted on arXiv 7 Apr 2022.
Builds on
71 of 87 references.
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