Capture and generalisation of close interaction with objects

Robust manipulation capture and retargeting has been a longstanding goal in both the fields of animation and robotics. In this thesis I describe a new approach to capture both the geometry and motion of interactions with objects, dealing with the problems of occlusion by the use of magnetic systems, and performing the reconstruction of the geometry by an RGB-D sensor alongside visual markers. This ‘interaction capture’ allows the scene to be described in terms of the spatial relationships between the character and the object using novel topological representations such as the Electric Parameters, which parametrise the outer space of an object using properties of the surface of the object. I describe the properties of these representations for motion generalisation and discuss how they can be applied to the problems of human-like motion generation and programming by demonstration. These generalised interactions are shown to be valid by demonstration of retargeting grasping and manipulation to robots with dissimilar kinematics and morphology using only local, gradient-based planning

More is Better: 3D Human Pose Estimation from Complementary Data Sources

Computer Vision (CV) research has been playing a strategic role in many different complex scenarios that are becoming fundamental components in our everyday life. From Augmented/Virtual reality (AR/VR) to Human-Robot interactions, having a visual interpretation of the surrounding world is the first and most important step to develop new advanced systems. As in other research areas, the boost in performance in Computer Vision algorithms has to be mainly attributed to the widespread usage of deep neural networks. Rather than selecting handcrafted features, such approaches identify which are the best features needed to solve a specific task, by learning them from a corpus of carefully annotated data. Such important property of these neural networks comes with a price: they need very large data collections to learn from. Collecting data is a time consuming and expensive operation that varies, being much harder for some tasks than others. In order to limit additional data collection, we therefore need to carefully design models that can extract as much information as possible from already available dataset, even those collected for neighboring domains. In this work I focus on exploring different solutions for and important research problem in Computer Vision, 3D human pose estimation, that is the task of estimating the 3D skeletal representation of a person characterized in an image/s. This has been done for several configurations: monocular camera, multi-view systems and from egocentric perspectives. First, from a single external front facing camera a semi-supervised approach is used to regress the set of 3D joint positions of the represented person. This is done by fully exploiting all of the available information at all the levels of the network, in a novel manner, as well as allowing the model to be trained with partially labelled data. A multi-camera 3D human pose estimation system is introduced by designing a network trainable in a semi-supervised or even unsupervised manner in a multiview system. Unlike standard motion-captures algorithm, demanding a long and time consuming configuration setup at the beginning of each capturing session, this novel approach requires little to none initial system configuration. Finally, a novel architecture is developed to work in a very specific and significantly harder configuration: 3D human pose estimation when using cameras embedded in a head mounted display (HMD). Due to the limited data availability, the model needs to carefully extract information from the data to properly generalize on unseen images. This is particularly useful in AR/VR use case scenarios, demonstrating the versatility of our network to various working conditions

Soulful bodies and superflat temporalities: a nomadology of the otaku database of world history at the ends of history

This thesis is a philosophical engagement with the popular, low, and vernacular theories of History performed and expressed within contemporary Japanese manga (‘comics’) and anime (‘limited animation’), and most importantly, in the global production and consumption of otaku (‘manga and anime fan’) cultural and media ecologies. My project is rooted in a reading of the post-structural theoretical inquiries of Gilles Deleuze in parallel with what media theorist McKenzie Wark calls ‘otaku philosophy’ to examine how both high and low theories articulate anxieties and fascinations with the global theoretical discourses on ‘the ends of History’ and the imminent demise of industrial modernity. The first portion of the thesis is dedicated to a reading of the Japanese counter-cultural manga movement called gekiga (‘dramatic pictures’). In traversing gekiga’s post-war lineages to its revival in the medievalism of otaku artists Miura Kentarō and Yukimura Makoto, the first part postulates on what an anti-modern, anti-historical approach – or what Deleuze and Guattari call a nomadology – might look and feel like as it is mediated in the manga form. The second portion of the thesis examines the way in which Japanese anime mobilises the philosophies of nomadology in its filmic form and transmedial properties. In a critical assessment of the anime works of the otaku-founded media corporation Type-Moon, this section explores the Fate series alongside Deleuzian film and media philosophies to explore the infinite potentialities and recursive limitations of otaku nomadologies as they materialise beyond the screen. By reassessing the rise of otaku culture as a vernacular, global, and cosmopolitan rise in the critique of modernity and History, this thesis hopes to explore how transcultural and transmedial fan philosophies of historicity, memory, and temporality can be recontextualised within current academic debates about the efficacy of post-national historiographic pedagogies explored in the fields of postcolonial studies, comparative studies, global studies, and media studies

Effects of errorless learning on the acquisition of velopharyngeal movement control

Session 1pSC - Speech Communication: Cross-Linguistic Studies of Speech Sound Learning of the Languages of Hong Kong (Poster Session)The implicit motor learning literature suggests a benefit for learning if errors are minimized during practice. This study investigated whether the same principle holds for learning velopharyngeal movement control. Normal speaking participants learned to produce hypernasal speech in either an errorless learning condition (in which the possibility for errors was limited) or an errorful learning condition (in which the possibility for errors was not limited). Nasality level of the participants’ speech was measured by nasometer and reflected by nasalance scores (in %). Errorless learners practiced producing hypernasal speech with a threshold nasalance score of 10% at the beginning, which gradually increased to a threshold of 50% at the end. The same set of threshold targets were presented to errorful learners but in a reversed order. Errors were defined by the proportion of speech with a nasalance score below the threshold. The results showed that, relative to errorful learners, errorless learners displayed fewer errors (50.7% vs. 17.7%) and a higher mean nasalance score (31.3% vs. 46.7%) during the acquisition phase. Furthermore, errorless learners outperformed errorful learners in both retention and novel transfer tests. Acknowledgment: Supported by The University of Hong Kong Strategic Research Theme for Sciences of Learning © 2012 Acoustical Society of Americapublished_or_final_versio

2017-2018 Course Catalog

2017-2018 Course Catalo

Association of Architecture Schools in Australasia

"Techniques and Technologies: Transfer and Transformation", proceedings of the 2007 AASA Conference held September 27-29, 2007, at the School of Architecture, UTS

2016-2017 Course Catalog

2016-2017 Course Catalo

2021-2022 Course Catalog

2021-2022 Course Catalo