Animal Reconstruction with 3D Morphable Models
type: CreativeWork
description: Across many sectors concerned with animal husbandry, there is a growing need for automated tools for continuously monitoring animals under our care. In farmyards, zoos, veterinary centres, animal research facilities and many others, humans are typically responsible for identifying signs of disease or distress within their animal populations. While this can be effective, a significant challenge is posed when a small number of humans are expected to care for large animal groups. Existing automated systems often install cameras and use computer vision algorithms to track motion and/or predict behaviours. However, these approaches are either low fidelity or cannot be readily applied to identifying ill health due to ethical concerns around data collection. This thesis proposes a solution based on using 3D morphable models (3DMMs) as a useful intermediary 3D animal representation, enabling identification of adverse events and reducing the task-specific data requirements for downstream behaviour/welfare prediction tasks. A 3DMM is a specially designed mesh, supplied with parameters that constrain deformations. Typical reconstruction pipelines tackle reconstructing a 3D articulated structure as estimating the 3DMM parameters per input frame. This thesis focuses on designing methods for 3D animal reconstruction, making use of suitable 3DMMs. We as humans are highly proficient at estimating the 3D structure of articulated subjects, such as animals or people. Even from a single image or video sequence, a human can with reasonable accuracy, predict the 3D locations of the animal’s limbs, estimate body proportions and even the camera’s location and viewing direction. However, these remain challenging tasks for computers. For this reason, 3DMMs provide a useful intermediate representation for downstream tasks since they help to explain the system’s output – if the recovered 3D model looks wrong, a human will notice immediately. Recently, great progress has been made in 3D human r…
abstract: Across many sectors concerned with animal husbandry, there is a growing need for automated tools for continuously monitoring animals under our care. In farmyards, zoos, veterinary centres, animal research facilities and many others, humans are typically responsible for identifying signs of disease or distress within their animal populations. While this can be effective, a significant challenge is posed when a small number of humans are expected to care for large animal groups. Existing automated systems often install cameras and use computer vision algorithms to track motion and/or predict behaviours. However, these approaches are either low fidelity or cannot be readily applied to identifying ill health due to ethical concerns around data collection. This thesis proposes a solution based on using 3D morphable models (3DMMs) as a useful intermediary 3D animal representation, enabling identification of adverse events and reducing the task-specific data requirements for downstream behaviour/welfare prediction tasks. A 3DMM is a specially designed mesh, supplied with parameters that constrain deformations. Typical reconstruction pipelines tackle reconstructing a 3D articulated structure as estimating the 3DMM parameters per input frame. This thesis focuses on designing methods for 3D animal reconstruction, making use of suitable 3DMMs. We as humans are highly proficient at estimating the 3D structure of articulated subjects, such as animals or people. Even from a single image or video sequence, a human can with reasonable accuracy, predict the 3D locations of the animal’s limbs, estimate body proportions and even the camera’s location and viewing direction. However, these remain challenging tasks for computers. For this reason, 3DMMs provide a useful intermediate representation for downstream tasks since they help to explain the system’s output – if the recovered 3D model looks wrong, a human will notice immediately. Recently, great progress has been made in 3D human r…
author: Biggs, Benjamin
language: eng
keywords: computer vision, morphable models, 3d reconstruction, animal tracking
license: https://www.rioxx.net/licenses/all-rights-reserved/
date published: 2021-12-20
date modified: 2026-07-22T22:24:21Z
provider: Cambridge University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Cambridge

PropertyValue
property ID: DOI
value: 10.17863/CAM.85609