Stan Szymanowicz

I am a PhD student at VGG group at University of Oxford, where I work on Computer Vision and Machine Learning with Andrea Vedaldi and Christian Rupprecht.

Before that, I worked at Microsoft Mixed Reality Lab with Matthew Johnson, Virginia Estellers and Julien Valentin. I received an MEng with Distinction from the University of Cambridge where I worked with Roberto Cipolla and James Charles.

Email  /  Google Scholar  /  Twitter  /  Github

Research

I am interested in many areas of Machine Learning - my research focuses on Computer Vision with an emphasis on 3D Vision. Recently, I have been exploring the intersection of 3D Computer Vision, Computer Graphics and Generative Models.

We design a fast (38FPS), simple, 2D network for single-view 3D reconstruction that represents shapes with 3D Gaussians. As a result, it can leverage Gaussian Splatting for rendering (588FPS), achieves state-of-the-art quality in several cases and is trains on just a single GPU.

Novel formulation of the denoising function in Diffusion Models lets us train 3D generative models from 2D data only. Our models can perform both few-view 3D reconstruction and 3D generation.

We unlock training NeRFs of 360◦ human heads from mobile phone captures. Synthetically-trained face keypoint detector allows to get a rough camera pose estimate which is then refined in the optimization process.

We can controllably animate Neural Radiance Fields by deforming tetrahedral cages. Tetrahedral connectivity allows us to run the animations in real-time.

We introduce an architecture for improved explainability of anomaly detection in CCTV videos.

We introduce a dataset and a method for detecting and explaining anomalies in CCTV videos.

Thank you to Jon Barron for the source code for the website!