OpenAI发布的视频生成模型Sora，效果炸裂

我有个预感，

Sora

正式上线

ChatGPT

的那一天，也是

GPT5

发布的那一天。

官方发布链接

：

https://openai.com/sora

说实话视频生成模型我用过，还不少，比如之前大火的

Runway

。

但是

OpenAI

这个大模型一出来，说实话就有点儿惊为天人了，下面这个动图是

Sora

的封面视频。

你看，这玩意像不像梦境？

虽然说大家在做产品宣发的时候都会拿最好的结果出来，这无可厚非，但是其他家的视频生成宣传图是真的不如

Sora

。

案例解释

这玩意有几个点非常的强，你看下面这个例子。

它对于复杂

Prompt

的理解能力

这是上面这个内容的

Prompt：

A stylish woman walks down a Tokyo street filled with warm glowing neon and animated city signage. She wears a black leather jacket, a long red dress, and black boots, and carries a black purse. She wears sunglasses and red lipstick. She walks confidently and casually. The street is damp and reflective, creating a mirror effect of the colorful lights. Many pedestrians walk about.

翻译：一位时尚的女人走在东京的街道上，街道上到处都是温暖的发光霓虹灯和动画城市标志。她身穿黑色皮夹克，红色长裙，黑色靴子，背着一个黑色钱包。她戴着墨镜，涂着红色口红。她自信而随意地走路。街道潮湿而反光，营造出五颜六色的灯光的镜面效果。许多行人四处走动。

你如果用过

绘画的话就知道，这个

prompt

含有的元素其实有很多，

绘画不一定能完美地复现，但是你看视频中呈现出来的元素，东京街道、各种霓虹灯，穿着打扮，都非常的契合。

更重要的是，连「自信而随意」这种东西都似乎非常的贴合，如果这是

Sora

的正常水平，我会觉得这个东西太恐怖了。

时长

这个视频足足有

秒，你可能会觉得是一段不到

分钟的视频而已。

但是我跟你说，现在主流的视频生成模型一般的生成时长在

秒钟之内，并不是不想生成长的，而是短短几秒能控制得住，一旦时间长了，就不知道会生成什么奇形怪状的东西出来了。

这个视频长度和大语言模型的输入输出长度一样，其实也是黄金标准。

一个大模型能处理

万字，另一个只能

万字，这水平不言而喻了。

画面的精细度

你看我圈出来的，这个人的脸部细节有多少，什么雀斑，各种色素什么的，是不是很多，这其实没什么。

但是下面这个就很恐怖了，这是一个镜头远一些的视频片段，你仔细看看她的脸。

是不是发现她脸上的那些细节基本上都还在，特别是左脸中间的那个雀斑？

还是那句话，别的视频生成模型，我也用过，但是像细节保存得这么好的，

Sora

现在是

Top1

。

技术概要

这个其实没有太多可以解释的，因为它的技术报告还没有放出来。

确定它也是用

Diffusion model

做的，简单点说，就是给一张图片加上雪花一样的噪音，然后再还原回去的过程，它的本质是物理上的扩散模型。

它原先是用来生成单张图片的，但是视频本身就是由图片组成的，比如一秒

=24

帧，每一张就是一张图片。

Sora

生成

秒视频，说白了就是生成

10*24 = 240

张图片，然后组合起来，就是一个视频。

原理就是这么的简单，别家也是用的差不多的算法，但是它的效果好，估计是训练数据好，由

OpenAI

的独家训练方法。

总结一下

⬇️

技术核心

·Sora是一种扩散模型，通过逐步去除初始静态噪声视频中的噪声来生成清晰视频。

功能特点

·一次性生成整个视频或对现有视频进行扩展，使其更长。

·通过同时预测多帧，解决了保持视频中主体一致性的挑战。

·使用transformer架构，提供了优秀的扩展性能。

数据处理方式

·视频和图像被分解为“补丁”（小数据单元），类似于GPT模型中的令牌。

·这种数据表示方法扩展了模型处理不同持续时间、分辨率和纵横比视觉数据的能力。

技术创新

·结合了DALL·E和GPT模型的研究成果。

·引入DALL·E 3的重新标题技术，为视觉训练数据生成描述性标题，以更准确地遵循用户的文本指令。

应用场景

·根据文本指令生成视频。

·从静止图像生成动态视频，精确动画化图像内容。

·扩展现有视频或填充缺失帧。

特性

1.能够生成具有多个角色、特定类型的运动以及主题和背景的准确细节的复杂场景。该模型不仅了解用户在提示中要求的内容，还了解这些东西在物理世界中的存在方式。

比如下面这个视频截图：

Prompt: A close up view of a glass sphere that has a zen garden within it. There is a small dwarf in the sphere who is raking the zen garden and creating patterns in the sand.

翻译：一个玻璃球的特写视图，里面有一个禅宗花园。球体中有一个小矮人，他正在禅宗花园里耙地，在沙子上创造图案。

生成出来的视频，考虑到了透过玻璃的画面会产生反射和折射，非常的精确还原了

Prompt

。

可以生成具有多个角色、特定类型的运动以及主题和背景的准确细节的复杂场景。该模型不仅了解用户在提示中要求的内容，还了解这些东西在物理世界中的存在方式。

比如这个庆祝春节的视频，里面不仅有舞龙，还有乌泱泱的人，非常精确的还原了

prompt

的内容，

缺点

难以准确模拟复杂场景的物理特性，并且可能无法理解因果关系的具体实例。例如，一个人可能会咬一口饼干，但之后，饼干可能没有咬痕。

该模型还可能混淆提示的空间细节，例如，左右混淆，并且可能难以精确描述随时间推移发生的事件，例如遵循特定的相机轨迹。

你看下面的这些狗子，会凭空出现。

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.wx-root[data-weui-theme=dark],body:not(.pages_skin_pc) body[data-weui-theme=dark]{--appmsgExtra-BG: #121212}}@media screen and (min-width:1024px){body:not(.pages_skin_pc)body:not(.pages_skin_pc){background:var(--weui-BG-2)}}@media screen and (min-width:1024px) and (prefers-color-scheme:dark){body:not(.pages_skin_pc) .wx-root[data-weui-mode=care]:not([data-weui-theme=light]),body:not(.pages_skin_pc) body[data-weui-mode=care]:not([data-weui-theme=light]){--weui-BG-0: #111;--weui-BG-1: #1e1e1e;--weui-BG-2: #191919;--weui-BG-3: #202020;--weui-BG-4: #404040;--weui-BG-5: #2c2c2c;--weui-FG-0: rgba(255, 255, 255, .85);--weui-FG-HALF: rgba(255, 255, 255, .65);--weui-FG-1: rgba(255, 255, 255, .55);--weui-FG-2: rgba(255, 255, 255, .35);--weui-FG-3: rgba(255, 255, 255, .1);--weui-FG-4: rgba(255, 255, 255, .15);--weui-FG-5: rgba(255, 255, 255, .1);--weui-RED: #fa5151;--weui-ORANGERED: #ff6146;--weui-ORANGE: #c87d2f;--weui-YELLOW: #cc9c00;--weui-GREEN: #74a800;--weui-LIGHTGREEN: #3eb575;--weui-BRAND: #07c160;--weui-BLUE: #10aeff;--weui-INDIGO: #1196ff;--weui-PURPLE: #8183ff;--weui-WHITE: rgba(255, 255, 255, .8);--weui-LINK: #7d90a9;--weui-TEXTGREEN: #259c5c;--weui-FG: #fff;--weui-BG: #000;--weui-TAG-TEXT-RED: rgba(250, 81, 81, .6);--weui-TAG-BACKGROUND-RED: rgba(250, 81, 81, .1);--weui-TAG-TEXT-ORANGE: rgba(250, 157, 59, .6);--weui-TAG-BACKGROUND-ORANGE: rgba(250, 157, 59, .1);--weui-TAG-TEXT-GREEN: rgba(6, 174, 86, .6);--weui-TAG-BACKGROUND-GREEN: rgba(6, 174, 86, .1);--weui-TAG-TEXT-BLUE: rgba(16, 174, 255, .6);--weui-TAG-BACKGROUND-BLUE: rgba(16, 174, 255, .1);--weui-TAG-TEXT-BLACK: rgba(255, 255, 255, .5);--weui-TAG-BACKGROUND-BLACK: rgba(255, 255, 255, .05)}}@media screen and (min-width:1024px) and (prefers-color-scheme:dark){body:not(.pages_skin_pc) .wx-root:not([data-weui-theme=light]),body:not(.pages_skin_pc) body:not([data-weui-theme=light]){--weui-BG-0: #111;--weui-BG-1: #1e1e1e;--weui-BG-2: #191919;--weui-BG-3: #202020;--weui-BG-4: 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rgba(0, 0, 0, .15);--weui-FG-5: rgba(0, 0, 0, .05);--weui-RED: #fa5151;--weui-ORANGERED: #ff6146;--weui-ORANGE: #fa9d3b;--weui-YELLOW: #ffc300;--weui-GREEN: #91d300;--weui-LIGHTGREEN: #95ec69;--weui-BRAND: #07c160;--weui-BLUE: #10aeff;--weui-INDIGO: #1485ee;--weui-PURPLE: #6467f0;--weui-WHITE: #fff;--weui-LINK: #576b95;--weui-TEXTGREEN: #06ae56;--weui-FG: #000;--weui-BG: #fff;--weui-TAG-TEXT-RED: rgba(250, 81, 81, .6);--weui-TAG-BACKGROUND-RED: rgba(250, 81, 81, .1);--weui-TAG-TEXT-ORANGE: #fa9d3b;--weui-TAG-BACKGROUND-ORANGE: rgba(250, 157, 59, .1);--weui-TAG-TEXT-GREEN: #06ae56;--weui-TAG-BACKGROUND-GREEN: rgba(6, 174, 86, .1);--weui-TAG-TEXT-BLUE: #10aeff;--weui-TAG-BACKGROUND-BLUE: rgba(16, 174, 255, .1);--weui-TAG-TEXT-BLACK: rgba(0, 0, 0, .5);--weui-TAG-BACKGROUND-BLACK: rgba(0, 0, 0, .05)}.wx-root,body{--weui-BTN-ACTIVE-MASK: rgba(0, 0, 0, .1)}.wx-root,body{--weui-BTN-DEFAULT-ACTIVE-BG: #e6e6e6}.wx-root,body{--weui-DIALOG-LINE-COLOR: rgba(0, 0, 0, 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#7d90a9;--weui-TEXTGREEN: #259c5c;--weui-FG: #fff;--weui-BG: #000;--weui-TAG-TEXT-RED: rgba(250, 81, 81, .6);--weui-TAG-BACKGROUND-RED: rgba(250, 81, 81, .1);--weui-TAG-TEXT-ORANGE: rgba(250, 157, 59, .6);--weui-TAG-BACKGROUND-ORANGE: rgba(250, 157, 59, .1);--weui-TAG-TEXT-GREEN: rgba(6, 174, 86, .6);--weui-TAG-BACKGROUND-GREEN: rgba(6, 174, 86, .1);--weui-TAG-TEXT-BLUE: rgba(16, 174, 255, .6);--weui-TAG-BACKGROUND-BLUE: rgba(16, 174, 255, .1);--weui-TAG-TEXT-BLACK: rgba(255, 255, 255, .5);--weui-TAG-BACKGROUND-BLACK: rgba(255, 255, 255, .05)}}@media(prefers-color-scheme:dark){.wx-root:not([data-weui-theme=light]),body:not([data-weui-theme=light]){--discussInput-BG: rgba(255, 255, 255, .03)}}@media(prefers-color-scheme:dark){.wx-root:not([data-weui-theme=light]),body:not([data-weui-theme=light]){--nickName-FG: #959595}}@media(prefers-color-scheme:dark){.wx-root:not([data-weui-theme=light]),body:not([data-weui-theme=light]){--weui-BG-0: #111;--weui-BG-1: #1e1e1e;--weui-BG-2: 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