End-to-end 3D face reconstruction with deep neural networks

Monocular 3D facial shape reconstruction from a single 2D facial image has been an active research area due to its wide applications. Inspired by the success of deep neural networks (DNN), we propose a DNN-based approach for End-to-End 3D FAce Reconstruction (UH-E2FAR) from a single 2D image. Different from recent works that reconstruct and refine the 3D face in an iterative manner using both an RGB image and an initial 3D facial shape rendering, our DNN model is end-to-end, and thus the complicated 3D rendering process can be avoided. Moreover, we integrate in the DNN architecture two components, namely a multi-task loss function and a fusion convolutional neural network (CNN) to improve facial expression reconstruction. With the multi-task loss function, 3D face reconstruction is divided into neutral 3D facial shape reconstruction and expressive 3D facial shape reconstruction. The neutral 3D facial shape is class-specific. Therefore, higher layer features are useful. In comparison, the expressive 3D facial shape favors lower or intermediate layer features. With the fusion-CNN, features from different intermediate layers are fused and transformed for predicting the 3D expressive facial shape. Through extensive experiments, we demonstrate the superiority of our end-to-end framework in improving the accuracy of 3D face reconstruction.Comment: Accepted to CVPR1

Levitating Particle Displays with Interactive Voxels

Levitating objects can be used as the primitives in a new type of display. We present levitating particle displays and show how research into object levitation is enabling a new way of presenting and interacting with information. We identify novel properties of levitating particle displays and give examples of the interaction techniques and applications they allow. We then discuss design challenges for these displays, potential solutions, and promising areas for future research

The development of a speech and language screening test for Maltese older persons

Aim: To develop the first screening test for Maltese speaking older adults with acquired language difficulties based on normative data, referred to in the study as Language Screening Test for the Elderly (LeST). Methodology: Normative data were collected from a representative sample of 77 elders living in the community, through cluster sampling from the five regions of Malta. Inclusion criteria: i) functional hearing abilities, ii) no history of neurological disorders, and iii) no cognitive impairment. Eighteen subjects were excluded. Factors taken into account: a) age (60 to 79 years and 80+ years), b) gender, c) educational background, d) literacy and e) bilingualism. A pilot study was carried out on 20 participants. Data collection was carried out at day centres by four speech language pathologists (SLPs). The LeST was also administered on ten subjects with aphasia and results were compared to the normal population. Results: Correlation and comparative statistical analysis of the data revealed that 1) scores were not affected by gender, age or locality, 2) p-values reach significance for factors related to bilingualism and literacy, and 3) the mean scores differ significantly between the control and test group. A cut-off point for receptive skills and one for expressive skills were also obtained. Conclusion: The test is valid for the purpose of language screening and would serve to ensure early identification of language impairment.peer-reviewe

Deep Markov Random Field for Image Modeling

Markov Random Fields (MRFs), a formulation widely used in generative image modeling, have long been plagued by the lack of expressive power. This issue is primarily due to the fact that conventional MRFs formulations tend to use simplistic factors to capture local patterns. In this paper, we move beyond such limitations, and propose a novel MRF model that uses fully-connected neurons to express the complex interactions among pixels. Through theoretical analysis, we reveal an inherent connection between this model and recurrent neural networks, and thereon derive an approximated feed-forward network that couples multiple RNNs along opposite directions. This formulation combines the expressive power of deep neural networks and the cyclic dependency structure of MRF in a unified model, bringing the modeling capability to a new level. The feed-forward approximation also allows it to be efficiently learned from data. Experimental results on a variety of low-level vision tasks show notable improvement over state-of-the-arts.Comment: Accepted at ECCV 201

Visualising Discourse Coherence in Non-Linear Documents

To produce coherent linear documents, Natural Language Generation systems have traditionally exploited the structuring role of textual discourse markers such as relational and referential phrases. These coherence markers of the traditional notion of text, however, do not work in non-linear documents: a new set of graphical devices is needed together with formation rules to govern their usage, supported by sound theoretical frameworks. If in linear documents graphical devices such as layout and formatting complement textual devices in the expression of discourse coherence, in non-linear documents they play a more important role. In this paper, we present our theoretical and empirical work in progress, which explores new possibilities for expressing coherence in the generation of hypertext documents

Displaying Lives: the Narrative of Objects in Biographical Exhibitions

Biographical exhibitions are a museum practice that asks for critical consideration. Grounding the argument in critical theory, social studies and museum theory, the article explores the narrative function of objects in biographical exhibitions by addressing the social significance of objects in relation to biography and their relevance when presented into an exhibition display. Central is the concept of objects as ‘biographical relics’ that are culturally fetishized in biographical narratives. This raises questions about biographical reliability and the cultural role that such objects plays in exhibition narratives as bearers of reality and as metonymical icons of the biographical subject. The article considers examples of biographical exhibitions of diverse figures such as Gregor Mendel, Madame de Pompadour and Roland Barthes, and the role that personal items, but also portraits and photographs, play in them