activity
20182025
most citedUnsupervised Generative 3D Shape Learning from Natural Images

49 citations · 49 across the 2 of their papers we have counts for

collaborators

6 papers

cs.CV2025

VoluMe -- Authentic 3D Video Calls from Live Gaussian Splat Prediction

Martin de La Gorce, Charlie Hewitt, Tibor Takacs +6

Virtual 3D meetings offer the potential to enhance copresence, increase engagement and thus improve effectiveness of remote meetings compared to standard 2D video calls. However, r…

cs.CV2024

Hairmony: Fairness-aware hairstyle classification

Givi Meishvili, James Clemoes, Charlie Hewitt +9

We present a method for prediction of a person's hairstyle from a single image. Despite growing use cases in user digitization and enrollment for virtual experiences, available met…

cs.CV2020

Video Representation Learning by Recognizing Temporal Transformations

Simon Jenni, Givi Meishvili, Paolo Favaro

We introduce a novel self-supervised learning approach to learn representations of videos that are responsive to changes in the motion dynamics. Our representations can be learned…

cs.CV201949 cited

Unsupervised Generative 3D Shape Learning from Natural Images

Attila Szabó, Givi Meishvili, Paolo Favaro

In this paper we present, to the best of our knowledge, the first method to learn a generative model of 3D shapes from natural images in a fully unsupervised way. For example, we d…

cs.CV2019

Learning to Have an Ear for Face Super-Resolution

Givi Meishvili, Simon Jenni, Paolo Favaro

We propose a novel method to use both audio and a low-resolution image to perform extreme face super-resolution (a 16x increase of the input size). When the resolution of the input…

cs.CV2018

Learning to Extract a Video Sequence from a Single Motion-Blurred Image

Meiguang Jin, Givi Meishvili, Paolo Favaro

We present a method to extract a video sequence from a single motion-blurred image. Motion-blurred images are the result of an averaging process, where instant frames are accumulat…