no code implementations • 10 Dec 2024 • Jack Saunders, Charlie Hewitt, Yanan Jian, Marek Kowalski, Tadas Baltrusaitis, Yiye Chen, Darren Cosker, Virginia Estellers, Nicholas Gyde, Vinay P. Namboodiri, Benjamin E Lundell
To overcome the limitations of existing datasets, we exploit the pixel-perfect nature of synthetic data to train a Gaussian Avatar prior.
no code implementations • 17 Oct 2024 • Lohit Petikam, Charlie Hewitt, Fatemeh Saleh, Tadas Baltrušaitis
Eyelid shape is integral to identity and likeness in human facial modeling.
no code implementations • 15 Oct 2024 • Charlie Hewitt, Fatemeh Saleh, Sadegh Aliakbarian, Lohit Petikam, Shideh Rezaeifar, Louis Florentin, Zafiirah Hosenie, Thomas J Cashman, Julien Valentin, Darren Cosker, Tadas Baltrusaitis
In this work, we introduce the first technique for marker-free, high-quality reconstruction of the complete human body, including eyes and tongue, without requiring any calibration, manual intervention or custom hardware.
no code implementations • 15 Oct 2024 • Givi Meishvili, James Clemoes, Charlie Hewitt, Zafiirah Hosenie, Xian Xiao, Martin de La Gorce, Tibor Takacs, Tadas Baltrusaitis, Antonio Criminisi, Chyna McRae, Nina Jablonski, Marta Wilczkowiak
We instead choose a classification approach which can represent the diversity of hairstyles required for a truly robust and inclusive system.
no code implementations • 6 Jun 2024 • Salvatore Esposito, Qingshan Xu, Kacper Kania, Charlie Hewitt, Octave Mariotti, Lohit Petikam, Julien Valentin, Arno Onken, Oisin Mac Aodha
We introduce a new generative approach for synthesizing 3D geometry and images from single-view collections.
no code implementations • 26 Jan 2024 • Hanz Cuevas-Velasquez, Charlie Hewitt, Sadegh Aliakbarian, Tadas Baltrušaitis
This presents a challenging scenario, as parts of the body often fall outside of the image or are occluded.
Ranked #6 on
Egocentric Pose Estimation
on UnrealEgo
no code implementations • 3 Jan 2023 • Charlie Hewitt, Tadas Baltrušaitis, Erroll Wood, Lohit Petikam, Louis Florentin, Hanz Cuevas Velasquez
Recent work has shown the benefits of synthetic data for use in computer vision, with applications ranging from autonomous driving to face landmark detection and reconstruction.
no code implementations • 5 Oct 2022 • Chirag Raman, Charlie Hewitt, Erroll Wood, Tadas Baltrusaitis
Recent advances in synthesizing realistic faces have shown that synthetic training data can replace real data for various face-related computer vision tasks.
1 code implementation • 5 Oct 2022 • Gwangbin Bae, Martin de La Gorce, Tadas Baltrusaitis, Charlie Hewitt, Dong Chen, Julien Valentin, Roberto Cipolla, Jingjing Shen
Such models are trained on large-scale datasets that contain millions of real human face images collected from the internet.
Ranked #2 on
Synthetic Face Recognition
on CPLFW
(Accuracy metric)
no code implementations • 6 Apr 2022 • Erroll Wood, Tadas Baltrusaitis, Charlie Hewitt, Matthew Johnson, Jingjing Shen, Nikola Milosavljevic, Daniel Wilde, Stephan Garbin, Chirag Raman, Jamie Shotton, Toby Sharp, Ivan Stojiljkovic, Tom Cashman, Julien Valentin
By fitting a morphable model to these dense landmarks, we achieve state-of-the-art results for monocular 3D face reconstruction in the wild.
Ranked #1 on
3D Face Reconstruction
on Florence
(RMSE Indoor metric)
no code implementations • ICCV 2021 • Erroll Wood, Tadas Baltrušaitis, Charlie Hewitt, Sebastian Dziadzio, Matthew Johnson, Virginia Estellers, Thomas J. Cashman, Jamie Shotton
We demonstrate that it is possible to perform face-related computer vision in the wild using synthetic data alone.
Ranked #2 on
Face Parsing
on Helen
(using extra training data)
no code implementations • 16 Jul 2020 • Tadas Baltrusaitis, Erroll Wood, Virginia Estellers, Charlie Hewitt, Sebastian Dziadzio, Marek Kowalski, Matthew Johnson, Thomas J. Cashman, Jamie Shotton
Analysis of faces is one of the core applications of computer vision, with tasks ranging from landmark alignment, head pose estimation, expression recognition, and face recognition among others.
no code implementations • 20 Nov 2018 • Charlie Hewitt, Marwa Mahmoud
This paper presents a novel feature set for shape-only leaf identification motivated by real-world, mobile deployment.
2 code implementations • 23 Jul 2018 • Charlie Hewitt, Hatice Gunes
Our results show that the proposed architectures retain similar performance to the dataset baseline while minimising storage requirements: achieving 58% accuracy for eight-class emotion classification and average RMSE of 0. 39 for valence/arousal prediction.
Human-Computer Interaction