To Authenticity, and Beyond! Building Safe and Fair Generative AI Upon the Three Pillars of Provenance.

Provenance facts, such as who made an image and how, can provide valuable context for users to make trust decisions about visual content. Against a backdrop of inexorable progress in generative AI for computer graphics, over two billion people will vote in public elections this year. Emerging standards and provenance enhancing tools promise to play an important role in fighting fake news and the spread of misinformation. In this article, we contrast three provenance enhancing technologies-metadata, fingerprinting, and watermarking-and discuss how we can build upon the complementary strengths of these three pillars to provide robust trust signals to support stories told by real and generative images. Beyond authenticity, we describe how provenance can also underpin new models for value creation in the age of generative AI. In doing so, we address other risks arising with generative AI such as ensuring training consent, and the proper attribution of credit to creatives who contribute their work to train generative models. We show that provenance may be combined with distributed ledger technology to develop novel solutions for recognizing and rewarding creative endeavor in the age of generative AI.

Deep learning with wearable based heart rate variability for prediction of mental and general health.

The ubiquity and commoditisation of wearable biosensors (fitness bands) has led to a deluge of personal healthcare data, but with limited analytics typically fed back to the user. The feasibility of feeding back more complex, seemingly unrelated measures to users was investigated, by assessing whether increased levels of stress, anxiety and depression (factors known to affect cardiac function) and general health measures could be accurately predicted using heart rate variability (HRV) data from wrist wearables alone. Levels of stress, anxiety, depression and general health were evaluated from subjective questionnaires completed on a weekly or twice-weekly basis by 652 participants. These scores were then converted into binary levels (either above or below a set threshold) for each health measure and used as tags to train Deep Neural Networks (LSTMs) to classify each health measure using HRV data alone. Three data input types were investigated: time domain, frequency domain and typical HRV measures. For mental health measures, classification accuracies of up to 83% and 73% were achieved, with five and two minute HRV data streams respectively, showing improved predictive capability and potential future wearable use for tracking stress and well-being.

Inpainting of Wide-Baseline Multiple Viewpoint Video.

We describe a non-parametric algorithm for multiple-viewpoint video inpainting. Uniquely, our algorithm addresses the domain of wide baseline multiple-viewpoint video (MVV) with no temporal look-ahead in near real time speed. A Dictionary of Patches (DoP) is built using multi-resolution texture patches reprojected from geometric proxies available in the alternate views. We dynamically update the DoP over time, and a Markov Random Field optimisation over depth and appearance is used to resolve and align a selection of multiple candidates for a given patch, this ensures the inpainting of large regions in a plausible manner conserving both spatial and temporal coherence. We demonstrate the removal of large objects (e.g., people) on challenging indoor and outdoor MVV exhibiting cluttered, dynamic backgrounds and moving cameras.

State of the "art": a taxonomy of artistic stylization techniques for images and video.

This paper surveys the field of nonphotorealistic rendering (NPR), focusing on techniques for transforming 2D input (images and video) into artistically stylized renderings. We first present a taxonomy of the 2D NPR algorithms developed over the past two decades, structured according to the design characteristics and behavior of each technique. We then describe a chronology of development from the semiautomatic paint systems of the early nineties, through to the automated painterly rendering systems of the late nineties driven by image gradient analysis. Two complementary trends in the NPR literature are then addressed, with reference to our taxonomy. First, the fusion of higher level computer vision and NPR, illustrating the trends toward scene analysis to drive artistic abstraction and diversity of style. Second, the evolution of local processing approaches toward edge-aware filtering for real-time stylization of images and video. The survey then concludes with a discussion of open challenges for 2D NPR identified in recent NPR symposia, including topics such as user and aesthetic evaluation.

The relationship between systemising and mental rotation and the implications for the extreme male brain theory of autism.

Within the Extreme Male Brain theory, Autism Spectrum Disorder is characterised as a deficit in empathising in conjunction with preserved or enhanced systemising. A male advantage in systemising is argued to underpin the traditional male advantage in mental rotation tasks. Mental rotation tasks can be separated into rotational and non-rotational components, and circulating testosterone has been found to consistently relate to the latter component. Systemising was found to correlate with mental rotation, specifically the non-rotational component(s) of the mental rotation task but not the rotational component of the task. Systemising also correlated with a proxy for circulating testosterone but not a proxy for prenatal testosterone. A sex difference was identified in systemising and the non-rotational aspect of the mental rotation task.

RTcams: a new perspective on nonphotorealistic rendering from photographs.

Abstract-We introduce a simple but versatile camera model that we call the Rational Tensor Camera (RTcam). RTcams are well principled mathematically and provably subsume several important contemporary camera models in both computer graphics and vision; their generality is one contribution. They can be used alone or compounded to produce more complicated visual effects. In this paper, we apply RTcams to generate synthetic artwork with novel perspective effects from real photographs. Existing Nonphotorealistic Rendering from Photographs (NPRP) is constrained to the projection inherent in the source photograph, which is most often linear. RTcams lift this restriction and so contribute to NPRP via multiperspective projection. This paper describes RTcams, compares them to contemporary alternatives, and discusses how to control them in practice. Illustrative examples are provided throughout.

Stroke surfaces: temporally coherent artistic animations from video.

The contribution of this paper is a novel framework for synthesizing nonphotorealistic animations from real video sequences. We demonstrate that, through automated mid-level analysis of the video sequence as a spatiotemporal volume--a block of frames with time as the third dimension--we are able to generate animations in a wide variety of artistic styles, exhibiting a uniquely high degree of temporal coherence. In addition to rotoscoping, matting, and novel temporal effects unique to our method, we demonstrate the extension of static nonphotorealistic rendering (NPR) styles to video, including painterly, sketchy, and cartoon shading. We demonstrate how this novel coherent shading framework may be combined with our earlier motion emphasis work to produce a comprehensive "Video Paintbox" capable of rendering complete cartoon-styled animations from video clips.