视觉与人工智能

生成式人工智能的最新突破使多种逼真的2D图像的可控生成成为可能，从而在艺术和设计等领域实现了变革性的应用。由于人类感知本质上是三维的，以可控的方式生成3D内容的能力可以解锁虚拟和增强现实、医疗保健、自动驾驶汽车、机器人等领域的众多应用，并具有广泛的影响。然而，我们还没有看到2D代游戏在3D领域取得同样的成功。在这次演讲中，我将概述在缩小这一差距的道路上面临的三个重要挑战。第一个挑战是以一种可控、富有表现力和紧凑的方式来表现3D世界。为此，我将描述一种以可分解和可解释的方式表示信号(如3D对象或场景)的新方法，该方法允许在可证明的保证下对信号施加约束。第二个挑战是从有限的二维观测中以可控的方式重塑三维世界。为此，我将描述一个框架，用于在3D场景中分解和操作对象，以及从新视图生成它们，仅给出2D训练数据。第三个挑战是为人类提供一个直观和灵活的界面，以可控的方式创建3D内容。为此，我将描述一种使用文本描述直观地对3D对象进行风格化的方法。 Lastly, I will conclude my talk with future directions on using controllable 3D generation for augmented reality, photorealistic simulations for applications such as autonomous vehicles, as well as enabling machines to better understand the world. Short bio: Sagie Benaim is a postdoctoral researcher at the Pioneer Center for AI, University of Copenhagen, advised by Prof. Serge Belongie. His research interests are in generative modeling, 3D content creation, few-shot learning, and representation learning. He received his PhD from the Computer Science department at Tel Aviv University, advised by Prof. Lior Wolf. Previously, he did his MSc at the University of Oxford and BSc at Imperial College London. During his PhD, he spent time as a research intern at Google.