MSR Thesis Defense

Abstract: Operation of quadrotors through high-speed, high-acceleration maneuvers remains a challenging problem due to the complex aerodynamics in this regime. While standard physical models suffice for control in near-hover conditions, the primary challenge in executing aggressive trajectories is obtaining a model for the quadrotor dynamics that adequately models the aerodynamic effects present, including lift, drag, [...]

Abstract: Deep generative models make visual content creation more accessible to novice users by automating the synthesis of diverse, realistic content based on a collected dataset. However, the current machine learning approaches miss several elements of the creative process -- the ability to synthesize things that go far beyond the data distribution and everyday experience, [...]

Abstract: Off-road driving is an important instance of navigation in unstructured environments, which is a key robotics problem with many applications, such as exploration, agriculture, disaster response and defense. The key challenge in off-road driving is to be able to take in high dimensional, multi-modal sensing data and use it to make intelligent decisions on [...]

Title: Total-Recon: Deformable Scene Reconstruction for Embodied View Synthesis Abstract: We explore the task of embodied view synthesis from monocular videos of deformable scenes. Given a minute-long RGBD video of people interacting with their pets, we render the scene from novel camera trajectories derived from in-scene motion of actors: (1) egocentric cameras that simulate the point [...]

Title: Learning Single Image 3D Reconstruction from Single-View Image Collections Abstract We present a framework for learning 3D object shapes and dense cross-object 3D correspondences from just an unaligned category-specific image collection. The 3D shapes are generated implicitly as deformations to a category-specific signed distance field and are learned in an unsupervised manner solely from unaligned [...]

Title: Audio-Visual State-Aware Representation Learning from Interaction-Rich Data Abstract In robotics and augmented reality, the input to the agent is a long stream of video from the first-person or egocentric point of view. Recently, there have been significant efforts to capture humans from their first-person/egocentric view interacting with their own environment as they go about [...]

Title: On Privacy and Personalization in Federated Learning: Analyses and Applications Abstract: Recent advances in machine learning often rely on large and centralized datasets. However, curating such data can be challenging when they hold private information, and policies/regulations may mandate that they remain distributed across data silos (e.g. mobile devices or hospitals). Federated learning (FL) [...]

Title: Seeing in 3D: Towards Generalizable 3D Visual Representations for Robotic Manipulation Abstract: Despite the recent progress in computer vision and deep learning, robot perception remains a tremendous challenge due to the variations of the objects and the scenes in manipulation tasks. Ideally, a robot trying to manipulate a new object should be able to [...]

Title: Efficient Spatially Sparse Inference for Conditional GANs and Diffusion Models Abstract: During image editing, existing deep generative models tend to re-synthesize the entire output from scratch, including the unedited regions. This leads to a significant waste of computation, especially for minor editing operations. In this work, we present Spatially Sparse Inference (SSI), a general-purpose technique [...]

Title: Spectral Unmixing and Mapping of Coral Reef Benthic Cover Abstract: Coral reefs are important to the global ecosystem and the local communities and wildlife that rely on the habitat they create. However, coral reefs are also in critical and rapid decline: reefs have degraded over recent decades and what remains is at increasing risk [...]