Learning to Reconstruct People in Clothing from a Single RGB Camera

We present a learning-based model to infer the personalized 3D shape of people from a few frames (1-8) of a monocular video in which the person is moving, in less than 10 seconds with a reconstruction accuracy of 5mm. Our model learns to predict the parameters of a statistical body model and instance displacements that add clothing and hair to the shape. The model achieves fast and accurate predictions based on two key design choices. First, by predicting shape in a canonical T-pose space, the network learns to encode the images of the person into pose-invariant latent codes, where the information is fused. Second, based on the observation that feed-forward predictions are fast but do not always align with the input images, we predict using both, bottom-up and top-down streams (one per view) allowing information to flow in both directions. Learning relies only on synthetic 3D data. Once learned, the model can take a variable number of frames as input, and is able to reconstruct shapes even from a single image with an accuracy of 6mm. Results on 3 different datasets demonstrate the efficacy and accuracy of our approach

Obtaining Formal Models through Non-Monotonic Refinement

When designing a model for formal verification, we want to\ud be certain that what we proved about the model also holds for the system we modelled. This raises the question of whether our model represents the system, and what makes us confident about this. By performing so called, non-monotonic refinement in the modelling process, we make the steps and decisions explicit. This helps us to (1) increase the confidence that the model represents the system, (2) structure and organize the communication with domain experts and the problem owner, and (3) identify rational steps made while modelling. We focus on embedded control systems

Traffic monitoring using image processing : a thesis presented in partial fulfillment of the requirements for the degree of Master of Engineering in Information and Telecommunications Engineering at Massey University, Palmerston North, New Zealand

Traffic monitoring involves the collection of data describing the characteristics of vehicles and their movements. Such data may be used for automatic tolls, congestion and incident detection, law enforcement, and road capacity planning etc. With the recent advances in Computer Vision technology, videos can be analysed automatically and relevant information can be extracted for particular applications. Automatic surveillance using video cameras with image processing technique is becoming a powerful and useful technology for traffic monitoring. In this research project, a video image processing system that has the potential to be developed for real-time application is developed for traffic monitoring including vehicle tracking, counting, and classification. A heuristic approach is applied in developing this system. The system is divided into several parts, and several different functional components have been built and tested using some traffic video sequences. Evaluations are carried out to show that this system is robust and can be developed towards real-time applications

Numerical simulation of long and slender cylinders vibrating in axial flow applied to the Myrrha reactor

Flow induced vibrations are an important concern in the design of nuclear reactors. One of the possible designs of the 4th generation nuclear reactors is a lead-cooled fast reactor of which MYYRHA is a prototype. The combination of high liquid density, flow velocity, low pitch-to-diameter ratio and the absence of grid spacers makes this design prone to flow induced vibrations. Although most vibrations are induced by cross flow, axial flow around this slender structure could also induce vibrations. In order to gain insight in the possible vibrations (either induced by cross flow, axial flow or an external excitation) this study examines the change of eigenmodes and frequencies of a bare rod due to the lead-bismuth flow. To do so partitioned simulations of the fluid structure interaction are performed in which the structure is initially perturbed according to an in-air eigenmode

Exploring Young Students' Functional Thinking

The Early Years Generalising Project (EYGP) involves Australian Years 1-4 (age 5-9) students and investigates how they grasp and express generalisations. This paper focuses on data collected from six Year 1 students in an exploratory study within a clinical interview setting that required students to identify function rules. Preliminary findings suggest that the use of gestures (both by students and interviewers), self-talk (by students), and concrete acting out, assisted students to reach generalisations and to begin to express these generalities. It also appears that as students become aware of the structure, their use of gestures and self- talk tended to decrease

Tex2Shape: Detailed Full Human Body Geometry From a Single Image

We present a simple yet effective method to infer detailed full human body shape from only a single photograph. Our model can infer full-body shape including face, hair, and clothing including wrinkles at interactive frame-rates. Results feature details even on parts that are occluded in the input image. Our main idea is to turn shape regression into an aligned image-to-image translation problem. The input to our method is a partial texture map of the visible region obtained from off-the-shelf methods. From a partial texture, we estimate detailed normal and vector displacement maps, which can be applied to a low-resolution smooth body model to add detail and clothing. Despite being trained purely with synthetic data, our model generalizes well to real-world photographs. Numerous results demonstrate the versatility and robustness of our method