Factors to Consider for Tailored Gamification

International audienceGamification is widely used to foster user motivation. Recent studies show that users can be more or less receptive to different game elements, based on their personality or player profile. Consequently, recent work on tailored gamification tries to identify links between user types and motivating game elements. However findings are very heterogeneous due to different contexts, different typologies to characterize users, and different implementations of game elements. Our work seeks to obtain more generalizable findings in order to identify the main factors that will support design choices when tailoring gamification to users' profiles and provide designers with concrete recommendations for designing tailored gamification systems. For this purpose, we ran a crowdsourced study with 300 participants to identify the motivational impact of game elements. Our study differs from previous work in three ways: first, it is independent from a specific user activity and domain; second, it considers three user typologies; and third, it clearly distinguishes motivational strategies and their implementation using multiple different game elements. Our results reveal that (1) different implementations of a same motivational strategy have different impacts on motivation, (2) dominant user type is not sufficient to differentiate users according to their preferences for game elements, (3) Hexad is the most appropriate user typology for tailored gamification and (4) the motiva-tional impact of certain game elements varies with the user activity or the domain of gamified systems

Adaptive coarse-to-fine quantization for optimizing rate-distortion of progressive mesh compression

International audienceWe propose a new connectivity-based progressivecompression approach for triangle meshes. The keyidea is to adapt the quantization precision to the resolutionof each intermediate mesh so as to optimizethe rate-distortion trade-off. This adaptation is automaticallydetermined during the encoding processand the overhead is efficiently encoded using geometricalprediction techniques. We also introducean optimization of the geometry coding by usinga bijective discrete rotation. Results show that ourapproach delivers a better rate-distortion behaviorthan both connectivity-based and geometry-basedcompression state of the art method

Efficient Decomposition of Image and Mesh Graphs by Lifted Multicuts

Formulations of the Image Decomposition Problem as a Multicut Problem (MP) w.r.t. a superpixel graph have received considerable attention. In contrast, instances of the MP w.r.t. a pixel grid graph have received little attention, firstly, because the MP is NP-hard and instances w.r.t. a pixel grid graph are hard to solve in practice, and, secondly, due to the lack of long-range terms in the objective function of the MP. We propose a generalization of the MP with long-range terms (LMP). We design and implement two efficient algorithms (primal feasible heuristics) for the MP and LMP which allow us to study instances of both problems w.r.t. the pixel grid graphs of the images in the BSDS-500 benchmark. The decompositions we obtain do not differ significantly from the state of the art, suggesting that the LMP is a competitive formulation of the Image Decomposition Problem. To demonstrate the generality of the LMP, we apply it also to the Mesh Decomposition Problem posed by the Princeton benchmark, obtaining state-of-the-art decompositions

PC-MSDM: A quality metric for 3D point clouds

International audienceIn this paper, we present PC-MSDM, an objective metric for visual quality assessment of 3D point clouds. This full-reference metric is based on local curvature statistics and can be viewed as an extension for point clouds of the MSDM metric suited for 3D meshes. We evaluate its performance on an open subjective dataset of point clouds compressed by octree pruning; results show that the proposed metric outperforms its counterparts in terms of correlation with mean opinion scores

Modeling and hexahedral meshing of cerebral arterial networks from centerlines

Computational fluid dynamics (CFD) simulation provides valuable information on blood flow from the vascular geometry. However, it requires extracting precise models of arteries from low-resolution medical images, which remains challenging. Centerline-based representation is widely used to model large vascular networks with small vessels, as it encodes both the geometric and topological information and facilitates manual editing. In this work, we propose an automatic method to generate a structured hexahedral mesh suitable for CFD directly from centerlines. We addressed both the modeling and meshing tasks. We proposed a vessel model based on penalized splines to overcome the limitations inherent to the centerline representation, such as noise and sparsity. The bifurcations are reconstructed using a parametric model based on the anatomy that we extended to planar n-furcations. Finally, we developed a method to produce a volume mesh with structured, hexahedral, and flow-oriented cells from the proposed vascular network model. The proposed method offers better robustness to the common defects of centerlines and increases the mesh quality compared to state-of-the-art methods. As it relies on centerlines alone, it can be applied to edit the vascular model effortlessly to study the impact of vascular geometry and topology on hemodynamics. We demonstrate the efficiency of our method by entirely meshing a dataset of 60 cerebral vascular networks. 92% of the vessels and 83% of the bifurcations were meshed without defects needing manual intervention, despite the challenging aspect of the input data. The source code is released publicly

Progressive Compression of Triangle Meshes

International audienceThis paper details the first publicly available implementation of the progressive mesh compression algorithm described in the paper entitled "Compressed Progressive Meshes" [R. Pajarola and J. Rossignac, IEEE Transactions on Visualization and Computer Graphics, 6 (2000), pp. 79-93]. Our implementation is generic, modular, and includes several improvements in the stopping criteria and final encoding. Given an input 2-manifold triangle mesh, an iterative simplification is performed, involving batches of edge collapse operations guided by an error metric. During this compression step, all the information necessary for the reconstruction (at the decompression step) is recorded and compressed using several key features: geometric quantization, prediction, and spanning tree encoding. Our implementation allowed us to carry out an experimental comparison of several settings for the key parameters of the algorithm: the local error metric, the position type of the resulting vertex (after collapse), and the geometric predictor. Source Code The proposed implementation is publicly available through the MEPP2 platform [20]. The algorithm can be used either as a command-line executable or integrated into the MEPP2 GUI. The source code is written in C++ and is accessible on the IPOL web page of this article 1 , as well as on the GitHub page of MEPP2 (MEPP-team/MEPP2 project 2)

Learning Boundary Edges for 3D-Mesh Segmentation

International audienceThis paper presents a 3D-mesh segmentation algorithm based on a learning approach. A large database of manually segmented 3D-meshes is used to learn a boundary edge function. The function is learned using a classifier which automatically selects from a pool of geometric features the most relevant ones to detect candidate boundary edges. We propose a processing pipeline that produces smooth closed boundaries using this edge function. This pipeline successively selects a set of candidate boundary contours, closes them and optimizes them using a snake movement. Our algorithm was evaluated quantitatively using two different segmentation benchmarks and was shown to outperform most recent algorithms from the state-of-the-art

Effect of material properties on emotion: a virtual reality study

IntroductionDesigners know that part of the appreciation of a product comes from the properties of its materials. These materials define the object’s appearance and produce emotional reactions that can influence the act of purchase. Although known and observed as important, the affective level of a material remains difficult to assess. While many studies have been conducted regarding material colors, here we focus on two material properties that drive how light is reflected by the object: its metalness and smoothness. In this context, this work aims to study the influence of these properties on the induced emotional response.MethodWe conducted a perceptual user study in virtual reality, allowing participants to visualize and manipulate a neutral object – a mug. We generated 16 material effects by varying it metalness and smoothness characteristics. The emotional reactions produced by the 16 mugs were evaluated on a panel of 29 people using James Russel’s circumplex model, for an emotional measurement through two dimensions: arousal (from low to high) and valence (from negative to positive). This scale, used here through VR users’ declarative statements allowed us to order their emotional preferences between all the virtual mugs.ResultStatistical results show significant positive effects of both metalness and smoothness on arousal and valence. Using image processing features, we show that this positive effect is linked to the increasing strength (i.e., sharpness and contrast) of the specular reflections induced by these material properties.DiscussionThe present work is the first to establish this strong relationship between specular reflections induced by material properties and aroused emotions