Animating in-game avatars using real time motion capture data is highly appealing and is becoming more widespread due to the accessibility of researchers and developers to consumer priced depth sensors [Microsoft Kinect 2010]. Depth sensors allow for a cheap and robust motion capture solution which can be naturally adapted to games. However, in spite of the many advantages of using real-time motion capture data for animating avatars in games, there are two major challenges. The first is due to the limitations of the current tracking techniques in producing smooth, noise free, accurate animation in real-time. The second, and more acute problem, stems from the fact that in most games, the movements of the animated avatar are expected to be more expressive than the player's actual movements. In such cases, one would like to visually enhance the player's motion to display exaggerated or even super-natural motions.

Abstract Many times, even if a crowd simulation looks good in general, there could be some specific individual behaviors which do not seem correct. Spotting such problems manually can become tedious, but ignoring them may harm the simulation's credibility. In this paper we present a data‐driven approach for evaluating the behaviors of individuals within a simulated crowd. Based on video‐footage of a real crowd, a database of behavior examples is generated. Given a simulation of a crowd, an analog analysis is performed on it, defining a set of queries, which are matched by a similarity function to the database examples. The results offer a possible objective answer to the question of how similar are the simulated individual behaviors to real observed behaviors. Moreover, by changing the video input one can change the context of evaluation. We show several examples of evaluating simulated crowds produced using different techniques and comprising of dense crowds, sparse crowds and flocks.

Abstract We present an example‐based crowd simulation technique. Most crowd simulation techniques assume that the behavior exhibited by each person in the crowd can be defined by a restricted set of rules. This assumption limits the behavioral complexity of the simulated agents. By learning from real‐world examples, our autonomous agents display complex natural behaviors that are often missing in crowd simulations. Examples are created from tracked video segments of real pedestrian crowds. During a simulation, autonomous agents search for examples that closely match the situation that they are facing. Trajectories taken by real people in similar situations, are copied to the simulated agents, resulting in seemingly natural behaviors.

Abstract In this paper we revisit the cells‐and‐portals visibility methods, originally developed for the special case of architectural interiors. We define an effectiveness measure for a cells‐and‐portals partition, and introduce a two‐pass algorithm that computes a cells‐and‐portals partition. The algorithm uses a simple heuristic that strives to create small portals as a means for generating an effective partition. The input to the algorithm is a set of half edges in 2D that can be extracted from a complex polygonal model. The first pass of the algorithm creates an initial partition, which is then refined by the second pass. We show that our method creates a partition that is more effective than the common BSP partition, even when the latter is further refined with the application of our second pass. Our cells‐and‐portals algorithm is designed to deal with arbitrarily oriented walls. The algorithm also supports outdoor scenes, where the vertical walls of the buildings serve as occluders and portals are extended above the buildings. We show that the extended portals allow an output‐sensitive rendering of large urban scenes. While most visibility algorithms use graphics hardware in order to cull hidden regions of the model, we examine the usefulness of a hardware‐assisted portal test as opposed to the conventional software test. Finally, since our two‐pass method is fully automatic and local, it supports incremental changes of the model by locally recomputing and updating the partition. We call our method ‘Breaking the Walls’ since it breaks out of indoor scenes to outdoor scenes, and allows walls to be broken interactively, with an instant updating of the partition. Copyright © 2006 John Wiley & Sons, Ltd.

In this paper, we revisit the cells-and-portals visibility methods, originally developed for the special case of architectural interiors. We define an effectiveness measure for a cells-and-portals partitioning, and introduce a two-pass algorithm that computes a cells-and-portals partition. The algorithm uses a simple heuristic that creates short portals as a mean for generating an effective partition. The input to the algorithm is a set of half edges in 2D that can be extracted from a complex polygonal model. The first pass of the algorithm creates an initial partition, which is then refined by the second pass. We show that our method creates a partition that is more effective than the common BSP partition, even when the latter is further refined with the application of our second pass. Our cells-and-portals algorithm is designed to deal with arbitrarily oriented walls. The algorithm also supports outdoor scenes, where the vertical walls of the buildings served as occluders and portals are extended above the buildings. We show that the extended portals allow an output-sensitive rendering of large urban scenes. Finally, since our two-pass method is fully automatic and local, it supports incremental changes of the model by locally recomputing and updating the partition. We call our method "Breaking the Walls" (BW) since it breaks out of indoor scenes to outdoor scenes, and allows walls to be broken interactively, with an instant updating of the partition.