Learning Generative Models of 3D Shapes and Environments

学习 3D 形状和环境的生成模型

• 批准号: RGPIN-2019-07098
• 负责人: Zhang, Hao
• 金额: $ 4.66万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=739307
• 关键词: Learning; Generative; Models; 3D; Shapes

One of the most intriguing and reoccurring questions in AI and computer science is when a machine can be considered to possess human-level intelligence. In its best-known version, the Turing Test judges the humanness of a machine by its ability to make natural language conversations. In 2014, a chatbot named Eugene was considered by many to have passed the test. So is Turing Test the best choice? Is it too easy? In the lesser-known "Lovelace Test", named after Lady Ada Lovelace, machines are judged by their creativity or originality. Lovelace pre-dated Turing by about 100 years and is often credited as the world's first computer programmer. In 1843, she remarked that computers cannot be thought to possess human intelligence until they can generate something original, which they were not programmed to do. Can a machine truly become creative? That is the ultimate question and the long-term pursuit would be to close the gap between machines and humans in creativity. In the shorter term, and as a computer graphics researcher, I want to tackle a more tangible problem first: to train machines to learn and execute generative models of 3D shapes and environments, where the outcomes do not have to be creative. While classical graphics mainly focuses on realistic image synthesis from explicit scene descriptions, in the new era of graphics, we wish to synthesize all forms of visual contents, where the inputs can be abstract (e.g., texts) or consist of only a set of exemplars. My current research objective is to advance data-driven visual content creation and geometric deep learning, making "big 3D data" a reality and fulfilling my four V's for data generation. Namely, the generated data should be in large volume and with cross-category variety, intra-category variation, and novelty - and ultimately, originality, to pass the Lovelace Test. In the next five years, I will advance the state of the art in developing and training generative models for 3D shapes and environments, enriching visual contents and design prototypes to serve applications in AR/VR, robotics, education, health, smart homes, and design and manufacturing. As well, I will keep pushing the boundary of computational creativity.

在AI和计算机科学中，最引人入胜，最重复的问题之一是可以认为机器具有人类水平的智能。图灵测试在其最著名的版本中，通过进行自然语言对话的能力来判断机器的人性。 2014年，许多人认为一个名叫Eugene的聊天机器人通过了测试。那么图灵测试是最佳选择吗？太容易了吗？在鲜为人知的“ Lovelace测试”中，以Ada Lovelace夫人的名字命名，机器的创造力或独创性来判断。 Lovelace先前的Turing大约100年，通常被认为是世界上第一位计算机程序员。在1843年，她指出，直到能够生成原始的东西，这些计算机才能拥有​​人类的智能，而这些智力是没有编程的。一台机器能真正发挥创造力吗？这是最终的问题，长期的追求是缩小机器和人类在创造力中的差距。在较短的任期中，作为计算机图形研究人员，我想首先解决一个更明显的问题：培训机器以学习和执行3D形状和环境的通用模型，而结果不必具有创造力。尽管经典图形主要集中于明确的场景描述中的现实图像综合，但在新的图形时代，我们希望综合所有形式的视觉内容，其中输入可以抽象为抽象（例如，文本）或仅由一组示例组成。我目前的研究目标是推进数据驱动的视觉内容创建和几何深度学习，使“大3D数据”成为现实，并实现我的四个V供数据生成。也就是说，生成的数据应大量且具有跨类别的多样性，类别内的变化以及新颖性 - 以及最终的创意，以通过Lovelace测试。在接下来的五年中，我将在为3D形状和环境的开发和培训通用模型中提高最新技术，并丰富视觉内容和设计原型，以在AR/VR，机器人技术，教育，健康，智能家庭以及设计和制造和制造业中为应用程序提供应用。同样，我将继续推动计算创造力的边界。