Publications and Patents

My full publication list is available on Google Scholar.

3D Multi-bodies: Fitting Sets of Plausible 3D Human Models to Ambiguous Image Data (NeurIPS 2020, Spotlight - Top 3%)

Benjamin Biggs, Sébastien Ehrhadt, Hanbyul Joo, Benjamin Graham, Andrea Vedaldi and David Novotny

We tackle the problem of obtaining dense 3D human reconstructions from single and partially occluded views. In such cases, the visual evidence is usually insufficient to identify a 3D reconstruction uniquely, so we recover a set of plausible and reconstructions, consistent with the input image. We train using a best-of-M loss, to which we add flexibility with a novel quantization scheme based on normalizing flows.

Who Left the Dogs Out? 3D Animal Reconstruction with Expectation Maximization in the Loop (ECCV 2020)

Benjamin Biggs, Oliver Boyne, James Charles, Andrew Fitzgibbon and Roberto Cipolla

We introduce a fully automatic, end-to-end system for 3D dog reconstruction trained using only weak 2D supervision. We use SMBLD, a new 3D deformable template model which includes a detailed shape prior learnt training using expectation maximization. We also release StanfordExtra: the largest dataset of 2D keypoints and segmentations for an animal category.

Creatures Great and SMAL: Recovering the Shape and Motion of Animals from Video (ACCV 2018, Oral - Top 5%)

Benjamin Biggs, Thomas Roddick, Andrew Fitzgibbon and Roberto Cipolla

A system to recover the 3D shape and motion of a wide variety of quadruped animals from video. We overcome the limited availability of animal motion capture data and ensure generalizability to real-world sequences by training on synthetic silhouettes. We apply our method on manually-segmented and automatically-segmented monocular animal videos and require no other form of user intervention.

Shape of You
Shape of You: Precise 3D Shape Estimations for Diverse Body Types (CVPR-W 2023, Oral)

Rohan Sakar, Achal Dave, Gerard Medioni and Benjamin Biggs

We improve 3D body shape estimation for diverse body types. While existing methods successfully estimate 3D pose, reliably estimating precise shape remains challenging. To address this gap, we propose new loss functions and a test-time optimization routine that can be readily integrated into parametric 3D human reconstruction pipelines.

Shape of You
On the Road to Large-Scale 3D Monocular Scene Reconstruction Using Deep Implicit Functions (ICCV-W 2021)

Thomas Roddick, Benjamin Biggs, Daniel Olmeda Reino and Roberto Cipolla

We propose deep implicit functions to reconstruct large-scale driving scenes. To avoid requiring watertight meshes for training, we instead use LiDAR to approximate ground truth occupancy labels. We evaluate on a real-world autonomous driving dataset and show incorporating geometric priors improves reconstruction quality.

Kinect Gowning Application

Benjamin Biggs, Patrick Hyett and Abhir Bhalerao

Computer vision application for verifying regulatory gowning procedures in collaboration with GlaxoSmithKline. Won departmental award for best third year dissertation at the University of Warwick.

Other Research

Benjamin Biggs - PhD Thesis
PhD Thesis - Benjamin Biggs

Supervisors: Roberto Cipolla & Andrew Fitzgibbon

This thesis focuses on designing methods for animal reconstruction, making use of 3D morphable models. Topics covered include: training 3D animal reconstruction algorithms with synthetic silhouette data, refining 3D shape priors in-the-loop and handling ambiguous input images.

Benjamin Biggs - PhD Thesis
StanfordExtra

Benjamin Biggs, Oliver Boyne, Andrew Fitzgibbon and Roberto Cipolla

12k labelled instances of dogs in-the-wild with 2D keypoint and segmentations.

RodentNet

Benjamin Biggs, Andrew Fitzgibbon and Roberto Cipolla

Behaviour and key point predictions at ~15fps by a deep learning architecture we refer to as RodentNet. Results shown on validation sequences from the SCORHE dataset.

© 2016 Creative Resume. All rights reserved | Design by W3layouts