There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

A morphable model for the synthesis of 3D faces

A survey of recent advances in CNN-based single image crowd counting and density estimation

Realistic hair modeling is a fundamental part of creating virtual humans in computer graphics. This paper surveys the state of the art in the major topics of hair modeling: hairstyling, hair simulation, and hair rendering. Because of the difficult, often unsolved problems that arise in alt these areas, a broad diversity of approaches is used, each with strengths that make it appropriate for particular applications. We discuss each of these major topics in turn, presenting the unique challenges facing each area and describing solutions that have been presented over the years to handle these complex issues. Finally, we outline some of the remaining computational challenges in hair modeling

/pdf/a-survey-on-hair-modeling-styling-simulation-and-rendering-1sdf5kj627.pdf

A Survey on Hair Modeling: Styling, Simulation, and Rendering

Although the widespread use of gaming for leisure purposes has been well documented, the use of games to support cultural heritage purposes, such as historical teaching and learning, or for enhancing museum visits, has been less well considered. The state-of-the-art in serious game technology is identical to that of the state-of-the-art in entertainment games technology. As a result, the field of serious heritage games concerns itself with recent advances in computer games, real-time computer graphics, virtual and augmented reality and artificial intelligence. On the other hand, the main strengths of serious gaming applications may be generalised as being in the areas of communication, visual expression of information, collaboration mechanisms, interactivity and entertainment. In this report, we will focus on the state-of-the-art with respect to the theories, methods and technologies used in serious heritage games. We provide an overview of existing literature of relevance to the domain, discuss the strengths and weaknesses of the described methods and point out unsolved problems and challenges. In addition, several case studies illustrating the application of methods and technologies used in cultural heritage are presented.

/pdf/developing-serious-games-for-cultural-heritage-a-state-of-4zaesd1wlb.pdf

Developing serious games for cultural heritage: a state-of-the-art review

Populated virtual environments need to be simulated with as much variety as possible. By identifying the most salient parts of the scene and characters, available resources can be concentrated where they are needed most. In this paper, we investigate which body parts of virtual characters are most looked at in scenes containing duplicate characters or clones. Using an eye-tracking device, we recorded fixations on body parts while participants were asked to indicate whether clones were present or not. We found that the head and upper torso attract the majority of first fixations in a scene and are attended to most. This is true regardless of the orientation, presence or absence of motion, sex, age, size, and clothing style of the character. We developed a selective variation method to exploit this knowledge and perceptually validated our method. We found that selective colour variation is as effective at generating the illusion of variety as full colour variation. We then evaluated the effectiveness of four variation methods that varied only salient parts of the characters. We found that head accessories, top texture and face texture variation are all equally effective at creating variety, whereas facial geometry alterations are less so. Performance implications and guidelines are presented.

http://isg.cs.tcd.ie/cosulliv/Pubs/Siggraph09McDonnell.pdf

Eye-catching crowds: saliency based selective variation

Fragment shaders for agent animation using finite state machines

Classic path finding algorithms are not adequate in real world path planning, where environment information is incomplete or dynamic and Markov Decision Processes have been used as an alternative. The problem with the MDP formalism is that its state space grows exponentially with the number of domain variables, and its inference methods grow with the number of available actions. To overcome this issue, we formulate a MDP solver in terms of matrix multiplications, based on the Value Iteration algorithm, thus we can take advantage of the graphic processor units (GPUs) to produce interactively obstacle-free paths in the form of an Optimal Policy. We also propose a hexagonal grid navigation space, that reduces the cardinality of the MDP state set. We present a performance analysis of our technique using embedded systems, desktop CPU and GPUs and its application in crowd simulation. Our GPU algorithm presents 90x speed up in desktop platforms, and 30x speed up in embedded systems in contrast with its CPU multi-threaded version.

A Parallel Solver for Markov Decision Process in Crowd Simulations

Large-scale crowd simulation and visualization is crucial for the next generation of interactive virtual environments. Current authoring techniques produce good results but are laborious, and demand valuable graphics memory and computational resources beyond the reach of consumer-level hardware. In this paper, we propose a technique for generating animatable characters for crowds that reduces memory requirements. The first step consists in reducing, segmenting and labeling a data set of virtual characters into simpler body parts; labeling information is then manually generated and used to correctly match different body parts in order to generate new characters. The second step comprises a method to embed the rig and skinning information into the texture space shared among the new characters. Additional methods using color, skin features, pattern, fat, wrinkle and textile fold maps are used to add more variety. Animation sequences are stored in auxiliary textures. These can be transferred between different characters, as well as versions of the same characters with different level of detail; such animations can be modified, and otherwise reused, increasing variety yet reducing memory requirements. We will demonstrate that our technique has four advantages: first, memory requirements are reduced by 91% when compared to traditional libraries; second, it can also generate previously nonexistent characters from the original data set; third, embedding the rig and skinning into texture space allows painless animation transfer between different characters and fourth, between different levels of detail of the same characters.

Reducing Memory Requirements for Diverse Animated Crowds

We present a set of algorithms for simulating and visualizing real-time crowds in GPU (Graphics Processing Units) clusters. First we will present crowd simulation and rendering techniques that take advantage of single GPU machines, then using as an example a wandering crowd behavior simulation algorithm, we explain how this kind of algorithms can be extended for their use in GPU cluster environments. We also present a visualization architecture that renders the simulation results using detailed 3D virtual characters. This architecture is adaptable in order to support the Barcelona Supercomputing Center (BSC) infrastructure. Results show that our algorithms are scalable in different hardware platforms including embedded systems, desktop GPUs and GPU clusters, in particular, the BSC's Minotauro cluster.

/pdf/simulating-and-visualizing-real-time-crowds-on-gpu-clusters-2fvnpm4co3.pdf

Benjamín Hernández

Papers

Eye-catching crowds: saliency based selective variation

Fragment shaders for agent animation using finite state machines

A Parallel Solver for Markov Decision Process in Crowd Simulations

Reducing Memory Requirements for Diverse Animated Crowds

Simulating and Visualizing Real-Time Crowds on GPU Clusters