10 citations · 25 across the 11 of their papers we have counts for
16 papers
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…
The Effectiveness of Temporal Dependency in Deepfake Video Detection
Will Rowan, Nick Pears
Deepfakes are a form of synthetic image generation used to generate fake videos of individuals for malicious purposes. The resulting videos may be used to spread misinformation, re…
FatNet: A Feature-attentive Network for 3D Point Cloud Processing
Chaitanya Kaul, Nick Pears, Suresh Manandhar
The application of deep learning to 3D point clouds is challenging due to its lack of order. Inspired by the point embeddings of PointNet and the edge embeddings of DGCNNs, we prop…