Attack on the clones: managing player perceptions of visual variety and believability in video game crowds

Crowds of non-player characters are increasingly common in contemporary video games. It is often the case that individual models are re-used, lowering visual variety in the crowd and potentially affecting realism and believability. This paper explores a number of approaches to increase visual diversity in large game crowds, and discusses a procedural solution for generating diverse non-player character models. This is evaluated using mixed methods, including a “clone spotting” activity and measurement of impact on computational overheads, in order to present a multi-faceted and adjustable solution to increase believability and variety in video game crowds

Feeling crowded yet?: Crowd simulations for VR

With advances in virtual reality technology and its multiple applications, the need for believable, immersive virtual environments is increasing. Even though current computer graphics methods allow us to develop highly realistic virtual worlds, the main element failing to enhance presence is autonomous groups of human inhabitants. A great number of crowd simulation techniques have emerged in the last decade, but critical details in the crowd's movements and appearance do not meet the standards necessary to convince VR participants that they are present in a real crowd. In this paper, we review recent advances in the creation of immersive virtual crowds and discuss areas that require further work to turn these simulations into more fully immersive and believable experiences.Peer ReviewedPostprint (author's final draft

Multi-Person Pose Estimation with Local Joint-to-Person Associations

Despite of the recent success of neural networks for human pose estimation, current approaches are limited to pose estimation of a single person and cannot handle humans in groups or crowds. In this work, we propose a method that estimates the poses of multiple persons in an image in which a person can be occluded by another person or might be truncated. To this end, we consider multi-person pose estimation as a joint-to-person association problem. We construct a fully connected graph from a set of detected joint candidates in an image and resolve the joint-to-person association and outlier detection using integer linear programming. Since solving joint-to-person association jointly for all persons in an image is an NP-hard problem and even approximations are expensive, we solve the problem locally for each person. On the challenging MPII Human Pose Dataset for multiple persons, our approach achieves the accuracy of a state-of-the-art method, but it is 6,000 to 19,000 times faster.Comment: Accepted to European Conference on Computer Vision (ECCV) Workshops, Crowd Understanding, 201

Human motion retrieval based on freehand sketch

In this paper, we present an integrated framework of human motion retrieval based on freehand sketch. With some simple rules, the user can acquire a desired motion by sketching several key postures. To retrieve efficiently and accurately by sketch, the 3D postures are projected onto several 2D planes. The limb direction feature is proposed to represent the input sketch and the projected-postures. Furthermore, a novel index structure based on k-d tree is constructed to index the motions in the database, which speeds up the retrieval process. With our posture-by-posture retrieval algorithm, a continuous motion can be got directly or generated by using a pre-computed graph structure. What's more, our system provides an intuitive user interface. The experimental results demonstrate the effectiveness of our method. © 2014 John Wiley & Sons, Ltd

Eye-tracktive: Measuring Attention to Body Parts When Judging Human Emotions

Virtual humans are often endowed with human-like characteristics to make them more appealing and engaging. Motion capture is a reliable way to represent natural motion on such characters, thereby allowing a wide range of animations to be automatically created and replicated. However, interpersonal differences in actors’ performances can be subtle and complex, yet have a strong effect on the human observer. Such effects can be very difficult to express quantitatively or indeed even qualitatively. We investigate two subjective human motion characteristics: attractiveness and distinctiveness. We conduct a perceptual experiment, where participants’ eye movements are tracked while they rate the motions of a range of actors. We found that participants fixate mostly on the torso, regardless of gait and actor sex, and very little on the limbs. However, they self-reported that they used hands, elbows and feet in their judgments, indicating a holistic approach to the problem

Skeleton-aware size variations in digital mannequins

The general trend in character modeling is toward the personalization of models with higher levels of visual realism. This becomes possible with the development of commodity computation resources that are capable of processing massive data in parallel across multiple processors. On the other hand, there is always a trade-off between the quantity of the model features that are simulated and the plausibility of the visual realism because of the limited computation resources. Also, to keep the resources' to be used efficiently within the other modeling approaches such as skin reflectance, aging, animation, etc., one must consider the efficiency of the method being used in the simulation. In this paper, we present an efficient method to customize the size of a human body model to personalize it with industry standard parameters. One of the major contributions of this method is that it is possible to generate a range of different size body models by using anthropometry surveys. This process is not limited by data-driven mesh deformation but also adapts the skeleton and motion to keep the consistency between different body layer