PhD Thesis Defense

Abstract: We explore how to infer the time-varying 3D structures of generic, deformable objects, and dynamic scenes from monocular videos. A solution to this problem is essential for virtual reality and robotics applications. However, inferring 4D structures given 2D observations is challenging due to its under-constrained nature. In a casual setup where there is neither [...]

Abstract: Humans learn by interacting with their surroundings using all of their senses. The first of these senses to develop is touch, and it is the first way that young humans explore their environment, learn about objects, and tune their cost functions (via pain or treats). Yet, robots are often denied this highly informative and [...]

Abstract:  In robotics, understanding the link between perception and action is pivotal. Typically, perception systems process sensory data into state representations like  segmentations and bounding boxes, which a planner uses to plan actions. However, this state estimation approach can fail in environments with partial observability, and in cases with challenging object properties like transparency and deformability.  [...]

Abstract: We study the problem of multi-human 3D reconstruction from videos captured in the wild. Human movements are dynamic, and accurately reconstructing them in various settings is crucial for developing immersive social telepresence, assistive humanoid robots, and augmented reality systems. However, creating such a system requires addressing fundamental issues with previous works regarding the data [...]

Abstract: In this thesis, we explore the use of Gaussian Representations in multiple application areas of computer vision and robotics. In particular, we design a ray-based differentiable renderer for 3D Gaussians that can be used to solve multiple classic computer vision problems in a unified manner. For example, we can reconstruct 3D shapes from color, [...]

Abstract: Realistic human avatars play a key role in immersive virtual telepresence. To reach a high level of realism, a human avatar needs to faithfully reflect human appearance. A human avatar should also be drivable and express natural motions. Existing works have made significant progress in building drivable realistic face avatars, but they rarely include [...]

Abstract: In this thesis, we aim to build photorealistic animatable avatars of humans wearing complex clothing in a data-driven manner. Such avatars will be a critical technology to enable future applications such as immersive telepresence in Virtual Reality (VR) and Augmented Reality (AR). Existing full-body avatars that jointly model geometry and view-dependent texture using Variational [...]

Abstract: In cluttered real-world workspaces, simple pick-and-place tasks for robot manipulators can be quite challenging to solve. Often there is no collision-free trajectory that allows the robot to grasp and extract a desired object from the scene. This requires motion planning algorithms to reason about rearranging some of the “movable” clutter in the scene so [...]

Abstract: Dexterity, the ability to perform complex interactions with the physical world, is at the core of robotics. However, existing research in robot manipulation has been focused on tasks that involve limited dexterity, such as pick-and-place. The motor skills of the robots are often quasi-static, have a predefined or limited sequence of contact events, and [...]

Abstract: In many practical applications of reinforcement learning (RL), it is expensive to observe state transitions from the environment. In the problem of plasma control for nuclear fusion, the motivating example of this thesis, determining the next state for a given state-action pair requires querying an expensive transition function which can lead to many hours [...]