【人工智能软件可以通过简单的草图实现整个数字世界】

【人工智能软件可以通过简单的草图实现整个数字世界】创建逼真的数字场景通常需要技巧,创造力和耐心。现在我们可以将工作放给AI算法。全球芯片制造商Nvidia开发了一种人工智能芯片,其不仅可以用于为虚拟现实自动生成虚拟环境,还可以用于教授关于全世界的自动驾驶汽车和机器人。Nvidia的研究人员使用标准的机器学习方法来识别视频场景中的不同对象:汽车,树木,建筑物等。然后,该团队使用所谓的生成对抗网络(GAN)来训练计算机填写逼真的3D图像。然后,系统可以输入场景的轮廓,显示不同物体的位置,并且它将填充令人惊叹的,略微闪烁的细节。效果令人印象深刻,即使其中一些物体偶尔看起来有点翘曲或扭曲。 “通过建立光与物体相互作用的方式渲染经典计算机图形,”Nvidia应用深度学习副总裁Bryan Catanzaro说,“未来可以用人工智能来改变渲染过程。”

https://www.technologyreview.com/s/612503/ai-software-can-dream-up-an-entire-digital-world-from-a-simple-sketch/

AI software can dream up an entire digital world from a simple sketch

Creating a virtual environment that looks realistic takes time and skill. The details have to be hand-crafted using a graphics chip that renders 3D shapes, appropriate lighting, and textures. The latest blockbuster video game, Red Dead Redemption 2, for example, took a team of around 1000 developers more than eight years to create—occasionally working 100-hour weeks. That kind of workload might not be required for much longer. A powerful new AI algorithm can dream up the photorealistic details of a scene on the fly.

Developed by chipmaker Nvidia, the software won’t just make life easier for software developers. It could also be used to auto-generate virtual environments for virtual reality or for teaching self-driving cars and robots about the world.

“We can create new sketches that have never been seen before and render those,” says Bryan Catanzaro, vice president of applied deep learning at Nvidia. “We’re actually teaching the model how to draw based on real video.”

Nvidia’s researchers used a standard machine-learning approach to identify different objects in a video scene: cars, trees, buildings, and so forth. The team then used what’s known as a generative adversarial network, or GAN, to train a computer to fill in realistic 3D imagery.

The system can then be fed the outline of a scene, showing where different objects are, and it will fill in stunning, slightly shimmering detail. The effect is impressive, even if some of these objects occasionally look a bit warped or twisted.

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“Classical computer graphics render by building up the way light interacts with objects,” says Catanzaro. “We wondered what we could do with artificial intelligence to change the rendering process.”

Catanzaro says the approach could lower the barrier for game design. Besides rendering whole scenes, the approach could be used to add a real person to a video game after feeding on a few minutes of video footage of the person in real life. He suggests that the approach could also be used to help render realistic settings for virtual reality, or to provide synthetic training data for autonomous vehicles or robots. “You can’t realistically get real training data for every situation that might pop up,” he says. The work was announced today at NeurIPS, a major AI conference in Montreal.

“This is interesting and impressive work,” says Michiel van de Panne, a professor at the University of British Columbia who specializes in machine learning and computer graphics. He notes that previous work involving GANs involved synthesizing simpler elements such as individual images or character motions.

“The work points the way to a very different way of creating animated imagery,” van de Panne says. “One with a different set of capabilities,” that are both less computationally intensive and could be interactive.

The Nvidia algorithm is just the latest in a dizzying procession of advances involving GANs. Invented by a Google researcher only a few years ago, GANs have emerged as a remarkable tool for synthesizing realistic, and often eerily strange imagery and audio. This trend promises to revolutionize computer graphics and special effects, and help artists and musicians imagine or develop new ideas. But it could also undermine public trust in video and audio evidence (see “Fake America great again”).

Catanzaro admits it could be misused. “This is a technology that could be used for a lot of things,” he says.


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