Measuring perceived challenge and demand in video games is crucial as these player experiences are essential to creating enjoyable games. Two recent measures that identified seemingly distinct structures of challenge (Challenge Originating from Recent Gameplay Interaction Scale (CORGIS) - cognitive, emotional, performative, decision-making) and demand (Video Game Demand Scale (VGDS) - cognitive, emotional, controller, exertional, social) have been theorised to overlap, reflecting the five-factor demand structure. To investigate the overlap between these two scales we compared a five (complete overlap) and nine-factor (no overlap) model by surveying 1,101 players asking them to recall their last gaming experience before completing CORGIS and VGDS. After failing to confirm both models, we conducted an exploratory factor analysis. Our findings reveal seven dimensions, where the five-factor VGDS model holds alongside two additional CORGIS dimensions of performative and decision-making, ultimately providing a more holistic understanding of the concepts whilst highlighting unique aspects of each approach.

We address the problem of camouflaging a 3D object from the many viewpoints that one might see it from. Given photographs of an object's surroundings, we produce a surface texture that will make the object difficult for a human to detect. To do this, we introduce several background matching algorithms that attempt to make the object look like whatever is behind it. Of course, it is impossible to exactly match the background from every possible viewpoint. Thus our models are forced to make trade-offs between different perceptual factors, such as the conspicuousness of the occlusion boundaries and the amount of texture distortion. We use experiments with human subjects to evaluate the effectiveness of these models for the task of camouflaging a cube, finding that they significantly outperform naïve strategies.

Boolean satisfiability (SAT) as a canonical NP-complete decision problem is one of the most important problems in computer science. In practice, real-world SAT sentences are drawn from a distribution that may result in efficient algorithms for their solution. Such SAT instances are likely to have shared characteristics and substructures. This work approaches the exploration of a family of SAT solvers as a learning problem. In particular, we relate polynomial time solvability of a SAT subset to a notion of margin between sentences mapped by a feature function into a Hilbert space. Provided this mapping is based on polynomial time computable statistics of a sentence, we show that the existance of a margin between these data points implies the existance of a polynomial time solver for that SAT subset based on the Davis-Putnam-Logemann-Loveland algorithm. Furthermore, we show that a simple perceptron-style learning rule will find an optimal SAT solver with a bounded number of training updates. We derive a linear time computable set of features and show analytically that margins exist for important polynomial special cases of SAT. Empirical results show an order of magnitude improvement over a state-of-the-art SAT solver on a hardware verification task.

This paper addresses scene understanding in the context of a moving camera, integrating semantic reasoning ideas from monocular vision with 3D information available through structure-from-motion. We combine geometric and photometric cues in a Bayesian framework, building on recent successes leveraging the indoor Manhattan assumption in monocular vision. We focus on indoor environments and show how to extract key boundaries while ignoring clutter and decorations. To achieve this we present a graphical model that relates photometric cues learned from labeled data, stereo photo-consistency across multiple views, and depth cues derived from structure-from-motion point clouds. We show how to solve MAP inference using dynamic programming, allowing exact, global inference in ~100 ms (in addition to feature computation of under one second) without using specialized hardware. Experiments show our system out-performing the state-of-the-art.

Though modern Visual Simultaneous Localisation and Mapping (vSLAM) systems are capable of localising robustly and efficiently even in the case of a monocular camera, the maps produced are typically sparse point-clouds that are difficult to interpret and of little use for higher-level reasoning tasks such as scene understanding or human- machine interaction. In this paper we begin to address this deficiency, presenting progress on expanding the competency of visual SLAM systems to build richer maps. Specifically, we concentrate on modelling indoor scenes using semantically meaningful surfaces and accompanying labels, such as “floor”, “wall”, and “ceiling” - an important step towards a representation that can support higher-level reasoning and planning. We leverage the Manhattan world assumption and show how to extract vanishing directions jointly across a video stream. We then propose a guided line detector that utilises known vanishing points to extract extremely subtle axis- aligned edges. We utilise recent advances in single view structure recovery to building geometric scene models and demonstrate our system operating on-line.