Emu Video: Factorizing Text-to-Video Generation by Explicit Image Conditioning
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Cited by 116 in Diffusion models, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
We present Emu Video, a text-to-video generation model that factorizes the generation into two steps: first generating an image conditioned on the text, and then generating a video conditioned on the text and the generated image. We identify critical design decisions--adjusted noise schedules for diffusion, and multi-stage training that enable us to directly generate high quality and high resolution videos, without requiring a deep cascade of models as in prior work. In human evaluations, our generated videos are strongly preferred in quality compared to all prior work--81% vs. Google's Imagen Video, 90% vs. Nvidia's PYOCO, and 96% vs. Meta's Make-A-Video. Our model outperforms commercial solutions such as RunwayML's Gen2 and Pika Labs. Finally, our factorizing approach naturally lends itself to animating images based on a user's text prompt, where our generations are preferred 96% over prior work.
Key facts. Cited by 255 papers in the map (data through 14 Oct 2026). Builds on 79 of its 90 references that are in the map. First posted on arXiv 17 Nov 2023.
Builds on
79 of 90 references.
References · 79
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- 2016Improved Techniques for Training GANscited by 2,970
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- 2022Imagen Video: High Definition Video Generation with Diffusion Modelscited by 1,778
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- 2023Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Modelscited by 1,476
- 2015Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcastingcited by 1,436
- 2022Scaling Autoregressive Models for Content-Rich Text-to-Image Generationcited by 1,295
- 2021Cascaded Diffusion Models for High Fidelity Image Generationcited by 1,163
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- 2022CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformerscited by 307
- 2015Deep multi-scale video prediction beyond mean square errorcited by 300
- 2022Flexible Diffusion Modeling of Long Videoscited by 294
- 2023Preserve Your Own Correlation: A Noise Prior for Video Diffusion Modelscited by 285
- 2023Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystackcited by 279
50 most cited of 79 All 79 (JSON)
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Citing papers per year: 2023: 9; 2024: 143; 2025: 79; 2026: 24.
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