Search Results for author: Adrian Hilton

Found 21 papers, 1 papers with code

Super-Resolution Appearance Transfer for 4D Human Performances

no code implementations31 Aug 2021 Marco Pesavento, Marco Volino, Adrian Hilton

Typically the requirement to frame cameras to capture the volume of a dynamic performance ($>50m^3$) results in the person occupying only a small proportion $<$ 10% of the field of view.


Attention-based Multi-Reference Learning for Image Super-Resolution

1 code implementation ICCV 2021 Marco Pesavento, Marco Volino, Adrian Hilton

A novel hierarchical attention-based sampling approach is introduced to learn the similarity between low-resolution image features and multiple reference images based on a perceptual loss.

Image Super-Resolution

SyDog: A Synthetic Dog Dataset for Improved 2D Pose Estimation

no code implementations31 Jul 2021 Moira Shooter, Charles Malleson, Adrian Hilton

Estimating the pose of animals can facilitate the understanding of animal motion which is fundamental in disciplines such as biomechanics, neuroscience, ethology, robotics and the entertainment industry.

Animal Pose Estimation

Multi-person Implicit Reconstruction from a Single Image

no code implementations CVPR 2021 Armin Mustafa, Akin Caliskan, Lourdes Agapito, Adrian Hilton

We present a new end-to-end learning framework to obtain detailed and spatially coherent reconstructions of multiple people from a single image.

Multi-View Consistency Loss for Improved Single-Image 3D Reconstruction of Clothed People

no code implementations29 Sep 2020 Akin Caliskan, Armin Mustafa, Evren Imre, Adrian Hilton

This paper introduces two advances to overcome this limitation: firstly a new synthetic dataset of realistic clothed people, 3DVH; and secondly, a novel multiple-view loss function for training of monocular volumetric shape estimation, which is demonstrated to significantly improve generalisation and reconstruction accuracy.

3D Human Shape Estimation 3D Reconstruction

Spectral Analysis Network for Deep Representation Learning and Image Clustering

no code implementations11 Sep 2020 Jinghua Wang, Adrian Hilton, Jianmin Jiang

This paper proposes a new network structure for unsupervised deep representation learning based on spectral analysis, which is a popular technique with solid theory foundations.

Image Clustering Representation Learning

Learning Dense Wide Baseline Stereo Matching for People

no code implementations2 Oct 2019 Akin Caliskan, Armin Mustafa, Evren Imre, Adrian Hilton

We show that it is possible to learn stereo matching from synthetic people dataset and improve performance on real datasets for stereo reconstruction of people from narrow and wide baseline stereo data.

Data Augmentation Stereo Matching

EdgeNet: Semantic Scene Completion from a Single RGB-D Image

no code implementations8 Aug 2019 Aloisio Dourado, Teofilo Emidio de Campos, Hansung Kim, Adrian Hilton

Semantic scene completion is the task of predicting a complete 3D representation of volumetric occupancy with corresponding semantic labels for a scene from a single point of view.

Edge Detection

Semantic Estimation of 3D Body Shape and Pose using Minimal Cameras

no code implementations8 Aug 2019 Andrew Gilbert, Matthew Trumble, Adrian Hilton, John Collomosse

We aim to simultaneously estimate the 3D articulated pose and high fidelity volumetric occupancy of human performance, from multiple viewpoint video (MVV) with as few as two views.

3D Human Pose Estimation

U4D: Unsupervised 4D Dynamic Scene Understanding

no code implementations ICCV 2019 Armin Mustafa, Chris Russell, Adrian Hilton

We introduce the first approach to solve the challenging problem of unsupervised 4D visual scene understanding for complex dynamic scenes with multiple interacting people from multi-view video.

3D Pose Estimation Instance Segmentation +2

Temporally Coherent General Dynamic Scene Reconstruction

no code implementations18 Jul 2019 Armin Mustafa, Marco Volino, Hansung Kim, Jean-yves Guillemaut, Adrian Hilton

Existing techniques for dynamic scene reconstruction from multiple wide-baseline cameras primarily focus on reconstruction in controlled environments, with fixed calibrated cameras and strong prior constraints.

Semantic Segmentation Virtual Reality

Volumetric performance capture from minimal camera viewpoints

no code implementations ECCV 2018 Andrew Gilbert, Marco Volino, John Collomosse, Adrian Hilton

We present a convolutional autoencoder that enables high fidelity volumetric reconstructions of human performance to be captured from multi-view video comprising only a small set of camera views.

4D Temporally Coherent Light-field Video

no code implementations30 Apr 2018 Armin Mustafa, Marco Volino, Jean-yves Guillemaut, Adrian Hilton

Evaluation of the proposed light-field scene flow against existing multi-view dense correspondence approaches demonstrates a significant improvement in accuracy of temporal coherence.

Scene Flow Estimation

Semantic Scene Completion Combining Colour and Depth: preliminary experiments

no code implementations13 Feb 2018 Andre Bernardes Soares Guedes, Teofilo Emidio de Campos, Adrian Hilton

Semantic scene completion is the task of producing a complete 3D voxel representation of volumetric occupancy with semantic labels for a scene from a single-view observation.

Semantically Coherent Co-Segmentation and Reconstruction of Dynamic Scenes

no code implementations CVPR 2017 Armin Mustafa, Adrian Hilton

Semantic co-segmentation exploits the coherence in semantic class labels both spatially, between views at a single time instant, and temporally, between widely spaced time instants of dynamic objects with similar shape and appearance.

3D Reconstruction

Temporally coherent 4D reconstruction of complex dynamic scenes

no code implementations CVPR 2016 Armin Mustafa, Hansung Kim, Jean-yves Guillemaut, Adrian Hilton

Sparse-to-dense temporal correspondence is integrated with joint multi-view segmentation and reconstruction to obtain a complete 4D representation of static and dynamic objects.

Scene Segmentation

General Dynamic Scene Reconstruction from Multiple View Video

no code implementations ICCV 2015 Armin Mustafa, Hansung Kim, Jean-yves Guillemaut, Adrian Hilton

The primary contributions of this paper are twofold: an automatic method for initial coarse dynamic scene segmentation and reconstruction without prior knowledge of background appearance or structure; and a general robust approach for joint segmentation refinement and dense reconstruction of dynamic scenes from multiple wide-baseline static or moving cameras.

Scene Segmentation

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