Translations:Diffusion Models Are Real-Time Game Engines/83/zh: Difference between revisions

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Diffusion models achieved state-of-the-art results in text-to-image generation (Saharia et al., [https://arxiv.org/html/2408.14837v1#bib.bib27 2022]; Rombach et al., [https://arxiv.org/html/2408.14837v1#bib.bib26 2022]; Ramesh et al., [https://arxiv.org/html/2408.14837v1#bib.bib25 2022]; Podell et al., [https://arxiv.org/html/2408.14837v1#bib.bib23 2023]), a line of work that has also been applied for text-to-video generation tasks (Ho et al., [https://arxiv.org/html/2408.14837v1#bib.bib14 2022]; Blattmann et al., [https://arxiv.org/html/2408.14837v1#bib.bib5 2023b]; [https://arxiv.org/html/2408.14837v1#bib.bib4 a]; Gupta et al., [https://arxiv.org/html/2408.14837v1#bib.bib9 2023]; Girdhar et al., [https://arxiv.org/html/2408.14837v1#bib.bib8 2023]; Bar-Tal et al., [https://arxiv.org/html/2408.14837v1#bib.bib3 2024]). Despite impressive advancements in realism, text adherence, and temporal consistency, video diffusion models remain too slow for real-time applications. Our work extends this line of work and adapts it for real-time generation conditioned autoregressively on a history of past observations and actions.

扩散模型在文本到图像生成中取得了最先进的成果（Saharia 等人，2022；Rombach 等人，2022；Ramesh 等人，2022；Podell 等人，2023），这一研究领域也被应用于文本到视频生成任务（Ho 等人，2022；Blattmann 等人，2023b；a；Gupta 等人，2023；Girdhar 等人，2023；Bar-Tal 等人，2024）。尽管在逼真性、文本依从性和时间一致性方面取得了显著进展，但视频扩散模型对于实时应用来说仍然过于缓慢。我们的工作扩展了这一研究，并使其适用于基于过去观察和动作历史的自回归条件下的实时生成。