1 code implementation • 28 Nov 2023 • James A. D. Gardner, Evgenii Kashin, Bernhard Egger, William A. P. Smith
We also introduce a novel `outside-in' method for computing differentiable sky visibility based on a neural directional distance function.
1 code implementation • 15 Nov 2023 • James A. D. Gardner, Bernhard Egger, William A. P. Smith
Training our model on a curated dataset of 1. 6K HDR environment maps of natural scenes, we compare it against traditional representations, demonstrate its applicability for an inverse rendering task and show environment map completion from partial observations.
no code implementations • 15 Apr 2023 • Mingrui Li, William A. P. Smith, Patrik Huber
Information about the environment (such as background and lighting) or changeable aspects of the face (such as pose, expression, presence of glasses, hat etc.)
no code implementations • 30 Mar 2023 • Finlay G. C. Hudson, William A. P. Smith
In this paper we propose a novel method for zero-shot, cross-domain image retrieval in which we make two key contributions.
no code implementations • 14 Jul 2022 • Meghna Asthana, William A. P. Smith, Patrik Huber
We propose to tackle the multiview photometric stereo problem using an extension of Neural Radiance Fields (NeRFs), conditioned on light source direction.
no code implementations • 7 Jun 2022 • James A. D. Gardner, Bernhard Egger, William A. P. Smith
Training our model on a curated dataset of 1. 6K HDR environment maps of natural scenes, we compare it against traditional representations, demonstrate its applicability for an inverse rendering task and show environment map completion from partial observations.
no code implementations • ECCV 2020 • Ye Yu, Abhimitra Meka, Mohamed Elgharib, Hans-Peter Seidel, Christian Theobalt, William A. P. Smith
Outdoor scene relighting is a challenging problem that requires good understanding of the scene geometry, illumination and albedo.
1 code implementation • 12 Feb 2021 • Ye Yu, William A. P. Smith
In this paper we show how to perform scene-level inverse rendering to recover shape, reflectance and lighting from a single, uncontrolled image using a fully convolutional neural network.
1 code implementation • ECCV 2020 • Dizhong Zhu, William A. P. Smith
Almost universally in computer vision, when surface derivatives are required, they are computed using only first order accurate finite difference approximations.
1 code implementation • CVPR 2020 • William A. P. Smith, Alassane Seck, Hannah Dee, Bernard Tiddeman, Joshua Tenenbaum, Bernhard Egger
In this paper, we bring together two divergent strands of research: photometric face capture and statistical 3D face appearance modelling.
1 code implementation • 18 Nov 2019 • Stylianos Ploumpis, Evangelos Ververas, Eimear O' Sullivan, Stylianos Moschoglou, Haoyang Wang, Nick Pears, William A. P. Smith, Baris Gecer, Stefanos Zafeiriou
Eye and eye region models are incorporated into the head model, along with basic models of the teeth, tongue and inner mouth cavity.
1 code implementation • 3 Sep 2019 • Bernhard Egger, William A. P. Smith, Ayush Tewari, Stefanie Wuhrer, Michael Zollhoefer, Thabo Beeler, Florian Bernard, Timo Bolkart, Adam Kortylewski, Sami Romdhani, Christian Theobalt, Volker Blanz, Thomas Vetter
In this paper, we provide a detailed survey of 3D Morphable Face Models over the 20 years since they were first proposed.
no code implementations • CVPR 2019 • Dizhong Zhu, William A. P. Smith
In this paper, we propose a hybrid depth imaging system in which a polarisation camera is augmented by a second image from a standard digital camera.
1 code implementation • CVPR 2019 • Stylianos Ploumpis, Haoyang Wang, Nick Pears, William A. P. Smith, Stefanos Zafeiriou
Three-dimensional Morphable Models (3DMMs) are powerful statistical tools for representing the 3D surfaces of an object class.
no code implementations • 18 Feb 2019 • Sarah Alotaibi, William A. P. Smith
We propose a novel biophysical and dichromatic reflectance model that efficiently characterises spectral skin reflectance.
1 code implementation • CVPR 2019 • Ye Yu, William A. P. Smith
By incorporating a differentiable renderer, our network can learn from self-supervision.
no code implementations • 7 Apr 2018 • Anil Bas, William A. P. Smith
In this configuration, our model learns an active appearance model and a means to fit the model from scratch with no supervision at all, even identity labels.
no code implementations • ICCV 2017 • Hang Dai, Nick Pears, William A. P. Smith, Christian Duncan
We present a fully automatic pipeline to train 3D Morphable Models (3DMMs), with contributions in pose normalisation, dense correspondence using both shape and texture information, and high quality, high resolution texture mapping.
no code implementations • ICCV 2017 • Silvia Tozza, William A. P. Smith, Dizhong Zhu, Ravi Ramamoorthi, Edwin R. Hancock
From a numerical point of view, we use a least-squares formulation of the discrete version of the problem.
1 code implementation • 23 Aug 2017 • Anil Bas, Patrik Huber, William A. P. Smith, Muhammad Awais, Josef Kittler
In this paper, we show how a 3D Morphable Model (i. e. a statistical model of the 3D shape of a class of objects such as faces) can be used to spatially transform input data as a module (a 3DMM-STN) within a convolutional neural network.
1 code implementation • 22 Aug 2017 • Anil Bas, William A. P. Smith
We show that this is not the case and that geometric information is an ambiguous cue.
no code implementations • CVPR 2017 • Hao Guan, William A. P. Smith
For interest point detection, we use a variant of the Accelerated Segment Test (AST) corner detector which operates on our geodesic grid.
no code implementations • 8 Sep 2016 • Alassane Seck, William A. P. Smith, Arnaud Dessein, Bernard Tiddeman, Hannah Dee, Abhishek Dutta
We present a practical approach to capturing ear-to-ear face models comprising both 3D meshes and intrinsic textures (i. e. diffuse and specular albedo).
no code implementations • CVPR 2016 • Chao Zhang, William A. P. Smith, Arnaud Dessein, Nick Pears, Hang Dai
In this paper we present a method for computing dense correspondence between a set of 3D face meshes using functional maps.
1 code implementation • 2 Feb 2016 • Anil Bas, William A. P. Smith, Timo Bolkart, Stefanie Wuhrer
We propose a fully automatic method for fitting a 3D morphable model to single face images in arbitrary pose and lighting.
no code implementations • ICCV 2015 • Chao Zhang, Behrend Heeren, Martin Rumpf, William A. P. Smith
In this paper we describe how to perform Principal Components Analysis in "shell space".
no code implementations • ICCV 2015 • Arnaud Dessein, William A. P. Smith, Richard C. Wilson, Edwin R. Hancock
We present an approach to modeling ear-to-ear, high-quality texture from one or more partial views of a face with possibly poor resolution and noise.