We tackle the problem of obtaining dense 3D human reconstructions from single and partially occluded views. In such cases, the visual evidence is usually insufficient to identify a 3D reconstruction uniquely, so we recover a set of plausible and reconstructions, consistent with the input image. We train using a best-of-M loss, to which we add flexibility with a novel quantization scheme based on normalizing flows.
We introduce a fully automatic, end-to-end system for 3D dog reconstruction trained using only weak 2D supervision. We use SMBLD, a new 3D deformable template model which includes a detailed shape prior learnt training using expectation maximization. We also release StanfordExtra: the largest dataset of 2D keypoints and segmentations for an animal category.
A system to recover the 3D shape and motion of a wide variety of quadruped animals from video. We overcome the limited availability of animal motion capture data and ensure generalizability to real-world sequences by training on synthetic silhouettes. We apply our method on manually-segmented and automatically-segmented monocular animal videos and require no other form of user intervention.
Computer vision application for verifying regulatory gowning procedures in collaboration with GlaxoSmithKline. Won departmental award for best third year dissertation at the University of Warwick.
Behaviour and key point predictions at ~15fps by a deep learning architecture we refer to as RodentNet. Results shown on validation sequences from the SCORHE dataset.