Learning Generative Models of 3D Shapes and Environments

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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.