10 citations · 25 across the 11 of their papers we have counts for
4 papers · 2 filters
Fake It Without Making It: Conditioned Face Generation for Accurate 3D Face Reconstruction
Will Rowan, Patrik Huber, Nick Pears +1
Accurate 3D face reconstruction from 2D images is an enabling technology with applications in healthcare, security, and creative industries. However, current state-of-the-art metho…
Text2Face: A Multi-Modal 3D Face Model
Will Rowan, Patrik Huber, Nick Pears +1
We present the first 3D morphable modelling approach, whereby 3D face shape can be directly and completely defined using a textual prompt. Building on work in multi-modal learning,…
Laplacian ICP for Progressive Registration of 3D Human Head Meshes
Nick Pears, Hang Dai, Will Smith +1
We present a progressive 3D registration framework that is a highly-efficient variant of classical non-rigid Iterative Closest Points (N-ICP). Since it uses the Laplace-Beltrami op…
Accurate Gaze Estimation using an Active-gaze Morphable Model
Hao Sun, Nick Pears
Rather than regressing gaze direction directly from images, we show that adding a 3D shape model can: i) improve gaze estimation accuracy, ii) perform well with lower resolution in…