MSR Speaking Qualifier
Carnegie Mellon University
Tanya Marwah – MSR Thesis Talk
Title: Generating 3D Human Animations from Single Monocular Images Abstract: Endowing AI systems with the ability to formulate a three-dimensional understanding of human appearance from a single RGB image is an important component technology for applications such as person re-identification, biometrics, virtual reality and augmented reality. However, jointly inferring the texture map and 3D [...]
Pragna Mannam – MSR Thesis Talk
Title: Model-free Sensorless Manipulation Abstract: This thesis is a study of 2D manipulation without sensing and planning, by exploring the effects of unplanned randomized action sequences on 2D object pose uncertainty. Our approach uses sensorless reorienting of an object to achieve a determined pose, regardless of the initial pose. Without using sensors and models [...]
Carnegie Mellon University
Gines Hidalgo Martinez – MSR Thesis Talk
Title: OpenPose: Whole-Body Pose Estimation Abstract: We present the first single-network approach for 2D whole-body (body, face, hand, and foot) pose estimation, capable of detecting an arbitrary number of people from in-the-wild images. Our method maintains constant real-time performance regardless of the number of people in the image. This network is trained in a [...]
Donglai Xiang – MSR Thesis Talk
Title: Monocular Total Capture: Pose Face, Body, and Hands in the Wild Abstract: We present the first method to capture the 3D total motion of a target person from a monocular view input. Given an image or a monocular video, our method reconstructs the motion from body, face, and fingers represented by a 3D deformable [...]
Carnegie Mellon University
Wentao Yuan – MSR Thesis Talk
Title: 3D Shape Completion and Canonical Pose Estimation with Structured Neural Networks Abstract: 3D point cloud is an efficient and flexible representation of 3D structures and the raw output of many 3D sensors. Recently, neural networks operating on point clouds have shown superior performance on various 3D understanding tasks, thanks to their power to [...]
Rawal Khirodkar – MSR Thesis Talk
Title: Leveraging Simulation for Computer Vision Abstract: A large amount of labeled data is required to train deep neural networks. The process of data annotation on such a large scale is expensive and time-consuming. A promising alternative in this regard is to use simulation to generate labeled synthetic data. However, a network trained solely [...]
Carnegie Mellon University
Maximilian Sieb – MSR Thesis Talk
Title: Visual Imitation Learning for Robot Manipulation Abstract: Imitation learning has been successfully applied to solve a variety of tasks in complex domains where an explicit reward function is not available. However, most imitation learning methods require access to the robot's actions during demonstration. This stands in a stark contrast to how we [...]
Carnegie Mellon University
Nikhil Jog – MSR Thesis Talk
Title: Highly Miniaturized Robots for Inspection of Small Nuclear Piping Abstract: Bomb making in the 20th century resulted in the creation of massive facilities to produce Uranium. As part of a multi-billion-dollar agenda, the measurement of radioactivity is required for the safe disposal of residual Uranium in piping. Manual techniques have proven too approximate, [...]
Chao Cao – MSR Thesis Talk
Title: Topological Path Planning for Mobile Robot Applications Abstract: Many path planning problems in mobile robot applications can be solved more efficiently in the topological space. By using the language of topology, the richer spatial information failed to captured by graph/grid-based map representations can be explicitly expressed and exploited. With that, it is possible [...]
Carnegie Mellon University
MSR Thesis Talk – Tao Chen
Title: Deep Reinforcement Learning with Prior Knowledge Abstract: Deep reinforcement learning has been applied to many domains from computer games, natural language processing, recommendation systems to robotics. While model-free reinforcement learning algorithms are promising approaches to learning policies without knowledge of the system dynamics, they usually require much more data. In this thesis, we [...]