Search Results for author:
Found 7 papers, 2 papers with code
Fake It Till You Make It: Face analysis in the wild using synthetic data alone
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)
CONFIG: Controllable Neural Face Image Generation
Our ability to sample realistic natural images, particularly faces, has advanced by leaps and bounds in recent years, yet our ability to exert fine-tuned control over the generative process has lagged behind.
Contrastive Learning for Lifted Networks
In this work we address supervised learning of neural networks via lifted network formulations.
A high fidelity synthetic face framework for computer vision
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.
VolTeMorph: Realtime, Controllable and Generalisable Animation of Volumetric Representations
The recent increase in popularity of volumetric representations for scene reconstruction and novel view synthesis has put renewed focus on animating volumetric content at high visual quality and in real-time.
Photo-realistic 360 Head Avatars in the Wild
To overcome this, we propose a novel landmark detector trained on synthetic data to estimate camera poses from 360 degree mobile phone videos of a human head for use in a multi-stage optimization process which creates a photo-realistic avatar.
BlendFields: Few-Shot Example-Driven Facial Modeling
Generating faithful visualizations of human faces requires capturing both coarse and fine-level details of the face geometry and appearance.
