Shubham Agrawal – MSR Thesis Talk
NSH 4305Title: 3D Face Geometry Capture Using Monocular Video Abstract: Accurate reconstruction of facial geometry has been one of the oldest tasks in computer vision. Despite being a long-studied problem, many modern methods fail to reconstruct realistic looking faces or rely on highly constrained environments for capture. High fidelity face reconstructions have so far been [...]
Tejas Khot – MSR Thesis Talk
NSH 4305Title: Unsupervised Learning for 3D Reconstruction and Blocks World Representation Abstract: Recovering the dense 3D structure of a scene from its images has been a long-standing goal in computer vision. Recent years have seen attempts of encoding richer priors into the geometry-based pipelines with the introduction of learning based methods. We argue that the form of 3D [...]
Tracking Beyond Detection
GHC 6501Abstract: The majority of existing vision-based methods perform multi-object tracking in the image domain. Yet, in mobile robotics and autonomous driving scenarios, pixel-precise object localization and trajectory estimation in 3D space are of fundamental importance. Furthermore, the leading paradigms for vision-based multi-object tracking and trajectory prediction heavily rely on object detectors and effectively limit tracking [...]
Yubo Zhang – MSR Thesis Talk
NSH 4305Title: A structured model for action detection Abstract: A dominant paradigm for learning-based approaches in computer vision is training generic models, such as ResNet for image recognition, or I3D for video understanding, on large datasets and allowing them to discover the optimal representation for the problem at hand. While this is an obviously attractive approach, [...]
Nilesh Kulkarni – MSR Thesis Talk
NSH 4305Title: Canonical Surface Mapping via Geometric Cycle Consistency Abstract: We explore the task of Canonical Surface Mapping (CSM). Specifically, given an image, we learn to map pixels on the object to their corresponding locations on an abstract 3D model of the category. But how do we learn such a mapping? A supervised approach would [...]