Multi-view multi-person 3D pose estimation aims to localize 3D skeleton joints for each person instance in a scene from multi-view camera inputs. It is a fundamental task that benefits many real-world applications (such assurveillance, sportscast, gaming and mixed reality) and ismainly tackled byreconstruction-based [6,14,4]andvolumetric [40]approaches inpreviousliterature, as showninFig.1(a)and(b).

We design a system that learns how to edit visual programs. Our edit network consumes a complete input program and a visual target. From this input, we task our network with predicting a local edit operation that could be applied to the input program to improve its similarity to the target.

What is your research area? My research topic, at a high level, hasn't changed much since my dissertation. It has always been the problem of physical scene understanding - building machines that see, reason about, and interact with the physical world. Besides learning algorithms, what are the levels of abstraction needed by Al systems in their representations, and where do they come from? I aim to answer these fundamental questions, drawing inspiration from nature, i.e., the physical world itself, and from human cognition.

This urges the development of more computation and communication efficient optimization algorithms.

With the built relation matrix and semantic-level representations, pixel representations can be augmented for pixel prediction.