Search Results for author: Jamie Shotton

Found 34 papers, 11 papers with code

WayveScenes101: A Dataset and Benchmark for Novel View Synthesis in Autonomous Driving

1 code implementation11 Jul 2024 Jannik Zürn, Paul Gladkov, Sofía Dudas, Fergal Cotter, Sofi Toteva, Jamie Shotton, Vasiliki Simaiaki, Nikhil Mohan

We present WayveScenes101, a dataset designed to help the community advance the state of the art in novel view synthesis that focuses on challenging driving scenes containing many dynamic and deformable elements with changing geometry and texture.

Autonomous Driving Benchmarking +1

CarLLaVA: Vision language models for camera-only closed-loop driving

no code implementations14 Jun 2024 Katrin Renz, Long Chen, Ana-Maria Marcu, Jan Hünermann, Benoit Hanotte, Alice Karnsund, Jamie Shotton, Elahe Arani, Oleg Sinavski

In this technical report, we present CarLLaVA, a Vision Language Model (VLM) for autonomous driving, developed for the CARLA Autonomous Driving Challenge 2. 0.

Autonomous Driving CARLA Leaderboard 2.0 +1

LangProp: A code optimization framework using Large Language Models applied to driving

1 code implementation18 Jan 2024 Shu Ishida, Gianluca Corrado, George Fedoseev, Hudson Yeo, Lloyd Russell, Jamie Shotton, João F. Henriques, Anthony Hu

LangProp automatically evaluates the code performance on a dataset of input-output pairs, catches any exceptions, and feeds the results back to the LLM in the training loop, so that the LLM can iteratively improve the code it generates.

Autonomous Driving Code Generation +2

GAIA-1: A Generative World Model for Autonomous Driving

no code implementations29 Sep 2023 Anthony Hu, Lloyd Russell, Hudson Yeo, Zak Murez, George Fedoseev, Alex Kendall, Jamie Shotton, Gianluca Corrado

Autonomous driving promises transformative improvements to transportation, but building systems capable of safely navigating the unstructured complexity of real-world scenarios remains challenging.

Autonomous Driving

Linking vision and motion for self-supervised object-centric perception

1 code implementation14 Jul 2023 Kaylene C. Stocking, Zak Murez, Vijay Badrinarayanan, Jamie Shotton, Alex Kendall, Claire Tomlin, Christopher P. Burgess

Object-centric representations enable autonomous driving algorithms to reason about interactions between many independent agents and scene features.

Autonomous Driving Object

Model-Based Imitation Learning for Urban Driving

1 code implementation14 Oct 2022 Anthony Hu, Gianluca Corrado, Nicolas Griffiths, Zak Murez, Corina Gurau, Hudson Yeo, Alex Kendall, Roberto Cipolla, Jamie Shotton

Our approach is the first camera-only method that models static scene, dynamic scene, and ego-behaviour in an urban driving environment.

Autonomous Driving Bird's-Eye View Semantic Segmentation +3

FastNeRF: High-Fidelity Neural Rendering at 200FPS

1 code implementation ICCV 2021 Stephan J. Garbin, Marek Kowalski, Matthew Johnson, Jamie Shotton, Julien Valentin

Recent work on Neural Radiance Fields (NeRF) showed how neural networks can be used to encode complex 3D environments that can be rendered photorealistically from novel viewpoints.

Mixed Reality Neural Rendering +1

A high fidelity synthetic face framework for computer vision

no code implementations16 Jul 2020 Tadas Baltrusaitis, Erroll Wood, Virginia Estellers, Charlie Hewitt, Sebastian Dziadzio, Marek Kowalski, Matthew Johnson, Thomas J. Cashman, Jamie Shotton

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.

Diversity Face Model +3

The Phong Surface: Efficient 3D Model Fitting using Lifted Optimization

no code implementations ECCV 2020 Jingjing Shen, Thomas J. Cashman, Qi Ye, Tim Hutton, Toby Sharp, Federica Bogo, Andrew William Fitzgibbon, Jamie Shotton

Realtime perceptual and interaction capabilities in mixed reality require a range of 3D tracking problems to be solved at low latency on resource-constrained hardware such as head-mounted devices.

Mixed Reality

High Resolution Zero-Shot Domain Adaptation of Synthetically Rendered Face Images

no code implementations ECCV 2020 Stephan J. Garbin, Marek Kowalski, Matthew Johnson, Jamie Shotton

In contrast to computer graphics approaches, generative models learned from more readily available 2D image data have been shown to produce samples of human faces that are hard to distinguish from real data.

Domain Adaptation Vocal Bursts Intensity Prediction

CONFIG: Controllable Neural Face Image Generation

2 code implementations ECCV 2020 Marek Kowalski, Stephan J. Garbin, Virginia Estellers, Tadas Baltrušaitis, Matthew Johnson, Jamie Shotton

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.

Attribute Face Model +2

DSAC - Differentiable RANSAC for Camera Localization

4 code implementations CVPR 2017 Eric Brachmann, Alexander Krull, Sebastian Nowozin, Jamie Shotton, Frank Michel, Stefan Gumhold, Carsten Rother

The most promising approach is inspired by reinforcement learning, namely to replace the deterministic hypothesis selection by a probabilistic selection for which we can derive the expected loss w. r. t.

Camera Localization Visual Localization

Fits Like a Glove: Rapid and Reliable Hand Shape Personalization

no code implementations CVPR 2016 David Joseph Tan, Thomas Cashman, Jonathan Taylor, Andrew Fitzgibbon, Daniel Tarlow, Sameh Khamis, Shahram Izadi, Jamie Shotton

We present a fast, practical method for personalizing a hand shape basis to an individual user's detailed hand shape using only a small set of depth images.

Decision Forests, Convolutional Networks and the Models in-Between

1 code implementation3 Mar 2016 Yani Ioannou, Duncan Robertson, Darko Zikic, Peter Kontschieder, Jamie Shotton, Matthew Brown, Antonio Criminisi

We present a systematic analysis of how to fuse conditional computation with representation learning and achieve a continuum of hybrid models with different ratios of accuracy vs. efficiency.

Image Classification Representation Learning

Opening the Black Box: Hierarchical Sampling Optimization for Estimating Human Hand Pose

no code implementations ICCV 2015 Danhang Tang, Jonathan Taylor, Pushmeet Kohli, Cem Keskin, Tae-Kyun Kim, Jamie Shotton

In this paper, we show that we can significantly improving upon black box optimization by exploiting high-level knowledge of the structure of the parameters and using a local surrogate energy function.

Hand Pose Estimation Image Generation

Model-Based Tracking at 300Hz Using Raw Time-of-Flight Observations

no code implementations ICCV 2015 Jan Stuhmer, Sebastian Nowozin, Andrew Fitzgibbon, Richard Szeliski, Travis Perry, Sunil Acharya, Daniel Cremers, Jamie Shotton

In this paper, we show how to perform model-based object tracking which allows to reconstruct the object's depth at an order of magnitude higher frame-rate through simple modifications to an off-the-shelf depth camera.

Object Tracking

Training CNNs with Low-Rank Filters for Efficient Image Classification

no code implementations20 Nov 2015 Yani Ioannou, Duncan Robertson, Jamie Shotton, Roberto Cipolla, Antonio Criminisi

Applying our method to a near state-of-the-art network for CIFAR, we achieved comparable accuracy with 46% less compute and 55% fewer parameters.

Classification General Classification +1

Learning an Efficient Model of Hand Shape Variation From Depth Images

no code implementations CVPR 2015 Sameh Khamis, Jonathan Taylor, Jamie Shotton, Cem Keskin, Shahram Izadi, Andrew Fitzgibbon

We represent the observed surface using Loop subdivision of a control mesh that is deformed by our learned parametric shape and pose model.

Filter Forests for Learning Data-Dependent Convolutional Kernels

no code implementations CVPR 2014 Sean Ryan Fanello, Cem Keskin, Pushmeet Kohli, Shahram Izadi, Jamie Shotton, Antonio Criminisi, Ugo Pattacini, Tim Paek

We propose 'filter forests' (FF), an efficient new discriminative approach for predicting continuous variables given a signal and its context.


Multi-Output Learning for Camera Relocalization

no code implementations CVPR 2014 Abner Guzman-Rivera, Pushmeet Kohli, Ben Glocker, Jamie Shotton, Toby Sharp, Andrew Fitzgibbon, Shahram Izadi

We formulate this problem as inversion of the generative rendering procedure, i. e., we want to find the camera pose corresponding to a rendering of the 3D scene model that is most similar with the observed input.

3D Reconstruction Camera Relocalization

Decision Jungles: Compact and Rich Models for Classification

no code implementations NeurIPS 2013 Jamie Shotton, Toby Sharp, Pushmeet Kohli, Sebastian Nowozin, John Winn, Antonio Criminisi

Randomized decision trees and forests have a rich history in machine learning and have seen considerable success in application, perhaps particularly so for computer vision.

Classification General Classification

GeoF: Geodesic Forests for Learning Coupled Predictors

no code implementations CVPR 2013 Peter Kontschieder, Pushmeet Kohli, Jamie Shotton, Antonio Criminisi

This paper presents a new and efficient forest based model that achieves spatially consistent semantic image segmentation by encoding variable dependencies directly in the feature space the forests operate on.

Image Segmentation Segmentation +2

KinectFusion: Real-Time Dense Surface Mapping and Tracking

no code implementations ISMAR 2011 Richard A. Newcombe, Shahram Izadi, Otmar Hilliges, David Molyneaux, David Kim, Andrew J. Davison, Pushmeet Kohli, Jamie Shotton, Steve Hodges, Andrew Fitzgibbon

We present a system for accurate real-time mapping of complex and arbitrary indoor scenes in variable lighting conditions, using only a moving low-cost depth camera and commodity graphics hardware.

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