# \[GN⁺\] OpenAI Sora: 텍스트에서 비디오를 생성하는 AI 모델 공개

**URL:** <https://discuss.pytorch.kr/t/gn-openai-sora-ai/3519>\
**Category:** 읽을거리&정보공유\
**Tags:** openai, text-to-video, geeknews, sora\
**Created:** [2월 16, 2024, 2:30오전 UTC](https://discuss.pytorch.kr/t/gn-openai-sora-ai/3519 "2024-02-16T02:30:00Z")\
**Posts on this page:** 1\
**Showing post:** 2

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**Author:** ![9bow](https://discuss.pytorch.kr/user_avatar/discuss.pytorch.kr/9bow/32/16301_2.png) [@9bow](https://discuss.pytorch.kr/u/9bow)\
**Post date:** [2월 20, 2024, 1:45오전 UTC](https://discuss.pytorch.kr/t/gn-openai-sora-ai/3519/2 "2024-02-20T01:45:20Z")

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[HuggingFace의 fffiloni라는 사용자](https://huggingface.co/fffiloni)가 OpenAI Sora의 참고 논문들을 정리하였습니다.

전체 목록은 아래 링크에서 확인하실 수 있습니다. 😃

> **[Sora Reference Papers - a fffiloni Collection](https://huggingface.co/collections/fffiloni/sora-reference-papers-65d0c8d4891646a27b84c4a8)**
>
> A collection of all papers referenced in OpenAI's "Video generation models as world simulators" technical report • openai.com/sora

* * *

아래 목록은 위 목록을 arXiv 링크로 변경해둔 것입니다. 😃

- [Unsupervised Learning of Video Representations using LSTMs / Paper•1502.04681•Published Feb 17, 2015](https://arxiv.org/abs/1502.04681)

- [Recurrent Environment Simulators / Paper•1704.02254•Published Apr 7, 2017](https://arxiv.org/abs/1704.02254)

- [World Models / Paper•1803.10122•Published Mar 28, 2018](https://arxiv.org/abs/1803.10122)

- [Generating Videos with Scene Dynamics / Paper•1609.02612•Published Sep 9, 2016](https://arxiv.org/abs/1609.02612)

- [MoCoGAN: Decomposing Motion and Content for Video Generation / Paper•1707.04993•Published Jul 17, 2017](https://arxiv.org/abs/1707.04993)

- [Adversarial Video Generation on Complex Datasets / Paper•1907.06571•Published Jul 16, 2019](https://arxiv.org/abs/1907.06571)

- [Generating Long Videos of Dynamic Scenes / Paper•2206.03429•Published Jun 8, 2022](https://arxiv.org/abs/2206.03429)

- [VideoGPT: Video Generation using VQ-VAE and Transformers / Paper•2104.10157•Published Apr 21, 2021•2](https://arxiv.org/abs/2104.10157)

- [NÜWA: Visual Synthesis Pre-training for Neural visUal World creAtion / Paper•2111.12417•Published Nov 24, 2021](https://arxiv.org/abs/2111.12417)

- [Imagen Video: High Definition Video Generation with Diffusion Models / Paper•2210.02303•Published Oct 5, 2022](https://arxiv.org/abs/2210.02303)

- [Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models / Paper•2304.08818•Published Apr 18, 2023•4](https://arxiv.org/abs/2304.08818)

- [Photorealistic Video Generation with Diffusion Models / Paper•2312.06662•Published Dec 12, 2023•20](https://arxiv.org/abs/2312.06662)

- [Language Models are Few-Shot Learners / Paper•2005.14165•Published May 29, 2020•7](https://arxiv.org/abs/2005.14165)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale / Paper•2010.11929•Published Oct 23, 2020•2](https://arxiv.org/abs/2010.11929)

- [ViViT: A Video Vision Transformer / Paper•2103.15691•Published Mar 30, 2021](https://arxiv.org/abs/2103.15691)

- [Masked Autoencoders Are Scalable Vision Learners / Paper•2111.06377•Published Nov 12, 2021•1](https://arxiv.org/abs/2111.06377)

- [Patch n' Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution / Paper•2307.06304•Published Jul 13, 2023•22](https://arxiv.org/abs/2307.06304)

- [High-Resolution Image Synthesis with Latent Diffusion Models / Paper•2112.10752•Published Dec 21, 2021•5](https://arxiv.org/abs/2112.10752)

- [Auto-Encoding Variational Bayes / Paper•1312.6114•Published Dec 21, 2013](https://arxiv.org/abs/1312.6114)

- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics / Paper•1503.03585•Published Mar 12, 2015](https://arxiv.org/abs/1503.03585)

- [Denoising Diffusion Probabilistic Models / Paper•2006.11239•Published Jun 20, 2020•1](https://arxiv.org/abs/2006.11239)

- [Improved Denoising Diffusion Probabilistic Models / Paper•2102.09672•Published Feb 19, 2021•1](https://arxiv.org/abs/2102.09672)

- [Diffusion Models Beat GANs on Image Synthesis / Paper•2105.05233•Published May 12, 2021](https://arxiv.org/abs/2105.05233)

- [Elucidating the Design Space of Diffusion-Based Generative Models / Paper•2206.00364•Published Jun 1, 2022•1](https://arxiv.org/abs/2206.00364)

- [Scalable Diffusion Models with Transformers / Paper•2212.09748•Published Dec 20, 2022•2](https://arxiv.org/abs/2212.09748)

- [Zero-Shot Text-to-Image Generation / Paper•2102.12092•Published Feb 24, 2021](https://arxiv.org/abs/2102.12092)

- [Scaling Autoregressive Models for Content-Rich Text-to-Image Generation / Paper•2206.10789•Published Jun 22, 2022•1](https://arxiv.org/abs/2206.10789)

- [Hierarchical Text-Conditional Image Generation with CLIP Latents / Paper•2204.06125•Published Apr 13, 2022•1](https://arxiv.org/abs/2204.06125)

- [SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations / Paper•2108.01073•Published Aug 3, 2021•4](https://arxiv.org/abs/2108.01073)

- [Attention Is All You Need / Paper•1706.03762•Published Jun 13, 2017•23](https://arxiv.org/abs/1706.03762)

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