引用本·阿弗莱克的话
英文标题：Quoting Ben Affleck
本·阿弗莱克表示，自己从年轻时就对计算机感兴趣，并在电影制作从胶片转向数字化后，进一步关注技术与视觉特效流程。 他指出，视觉特效行业多年来一直在使用机器学习，并提到自己能够编写一些 Python 脚本。 他以卷积神经网络、张量、边缘检测和特征提取为例，解释了机器学习如何帮助识别画面结构，从而更高效地处理绿幕替换。 这段表述反映出影视制作中的机器学习应用早于当前 Transformer 和生成式 AI 热潮，并持续影响视觉内容生产工作流。
中文
2026 年 10 月 7 日
我从年轻时起就一直对计算机有些兴趣。后来，当电影开始从模拟胶片转向数字技术时，我对其中的技术层面变得更感兴趣。多年来，视觉特效工作流一直包含机器学习。
所以我能写一些挺烂的 Python 脚本之类的东西，因为使用卷积神经网络——它算是 Transformer 能力的前身，Transformer 只是在同一时间进行更多计算——你会处理所谓的张量。张量只是把视觉图像以数字形式进行转换，比如批次编号、帧编号，以及每一帧中每个像素的红、绿、蓝数值。
对于张量，你会使用卷积神经网络来识别其中的模式，这些模式能够揭示所谓的边缘检测或特征提取；也就是说，识别出足够多的模式，从而知道窗台在哪里，因此我们就能更轻松地移除绿幕图像，并用其他内容替换它。
— 本·阿弗莱克，Python 爱好者
English / 原文
7th October 2026
I've always been kind of into computers since I was young. And then when film started to move from analog film to digital, I became more interested in that aspect of it. And the visual effects workflow for many years has included machine learning.
So I can write like pretty shitty Python scripts and stuff like that because with convolutional neural networks, which were the sort of precursors to what the transformer can do, which is just much more computation simultaneously, you would do things like look at what's called a tensor, which is just the numerical translation of a visual image in numbers — like the batch number, the frame number, the red, green, and blue values of each pixel in each frame.
And a tensor, you use a convolutional neural network to identify patterns in that that would reveal what's called edge detection or feature extraction, which is just identifying patterns enough to know like this is where the window ledge is, so we can more easily take the green screen image out and replace it with something.
— Ben Affleck, Pythonista