Optimal Path Maps on the GPU.

We introduce a new method for computing optimal path maps on the GPU using OpenGL shaders. Our method explores GPU rasterization as a way to propagate optimal costs on a polygonal 2D environment, producing optimal path maps which can efficiently be queried at run-time. Our method is implemented entirely with GPU shaders, does not require pre-computation, addresses optimal path maps with multiple points and line segments as sources, and introduces a new optimal path map concept not addressed before: maps with weights at vertices representing possible changes in traversal speed. The produced maps offer new capabilities not explored by previous navigation representations and at the same time address paths with global optimality, a characteristic which has been mostly neglected in animated virtual environments. The proposed path maps partition the input environment into the regions sharing a same parent point along the shortest path to the closest source, taking into account possible speed changes at vertices. The proposed approach is particularly suitable for the animation of multiple agents moving toward the entrances or exits of a virtual environment, a situation which is efficiently represented with the proposed path maps.

Planning Motions and Placements for Virtual Demonstrators.

In order to deliver information effectively, virtual human demonstrators must be able to address complex spatial constraints and at the same time replicate motion coordination patterns observed in human-human interactions. We introduce in this paper a whole-body motion planning and synthesis framework that coordinates locomotion, body positioning, action execution and gaze behavior for generic demonstration tasks among obstacles. Human-like solutions are achieved with a coordination model extracted from experiments with human subjects. Given an observer location and a target demonstration to be performed, the proposed planner automatically identifies body placements respecting visibility constraints, locomotion accessibility, and action feasibility among obstacles. Actions are modeled with clusters of example motions and a fast collision avoidance procedure in blending space is introduced to avoid nearby obstacles when needed. Locomotion towards new placements integrates planning among obstacles and is based on a motion capture database organized for efficient synthesis of motions with precise path following and arrival constraints. The proposed solution introduces effective approaches for modeling and solving complex demonstrative tasks for interactive applications.

The Effects of Avatars, Stereo Vision and Display Size on Reaching and Motion Reproduction.

Thanks to recent advances on motion capture devices and stereoscopic consumer displays, animated virtual characters can now realistically interact with users in a variety of applications. We investigate in this paper the effect of avatars, stereo vision and display size on task execution in immersive virtual environments. We report results obtained with three experiments in varied configurations that are commonly used in rehabilitation applications. The first experiment analyzes the accuracy of reaching tasks under different system configurations: with and without an avatar, with and without stereo vision, and employing a 2D desktop monitor versus a large multi-tile visualization display. The second experiment analyzes the use of avatars and user-perspective stereo vision on the ability to perceive and subsequently reproduce motions demonstrated by an autonomous virtual character. The third experiment evaluates the overall user experience with a complete immersive user interface for motion modeling by direct demonstration. Our experiments expose and quantify the benefits of using stereo vision and avatars, and show that the use of avatars improve the quality of produced motions and the resemblance of replicated msotions; however, direct interaction in user-perspective leads to tasks executed in less time and to targets more accurately reached. These and additional tradeoffs are important for the effective design of avatar-based training systems.

Designing antibiotic cycling strategies by determining and understanding local adaptive landscapes.

The evolution of antibiotic resistance among bacteria threatens our continued ability to treat infectious diseases. The need for sustainable strategies to cure bacterial infections has never been greater. So far, all attempts to restore susceptibility after resistance has arisen have been unsuccessful, including restrictions on prescribing [1] and antibiotic cycling [2], [3]. Part of the problem may be that those efforts have implemented different classes of unrelated antibiotics, and relied on removal of resistance by random loss of resistance genes from bacterial populations (drift). Here, we show that alternating structurally similar antibiotics can restore susceptibility to antibiotics after resistance has evolved. We found that the resistance phenotypes conferred by variant alleles of the resistance gene encoding the TEM ß-lactamase (bla(TEM)) varied greatly among 15 different ß-lactam antibiotics. We captured those differences by characterizing complete adaptive landscapes for the resistance alleles bla(TEM-50) and bla(TEM-85), each of which differs from its ancestor bla(TEM-1) by four mutations. We identified pathways through those landscapes where selection for increased resistance moved in a repeating cycle among a limited set of alleles as antibiotics were alternated. Our results showed that susceptibility to antibiotics can be sustainably renewed by cycling structurally similar antibiotics. We anticipate that these results may provide a conceptual framework for managing antibiotic resistance. This approach may also guide sustainable cycling of the drugs used to treat malaria and HIV.

Analyzing locomotion synthesis with feature-based motion graphs.

We propose feature-based motion graphs for realistic locomotion synthesis among obstacles. Among several advantages, feature-based motion graphs achieve improved results in search queries, eliminate the need of postprocessing for foot skating removal, and reduce the computational requirements in comparison to traditional motion graphs. Our contributions are threefold. First, we show that choosing transitions based on relevant features significantly reduces graph construction time and leads to improved search performances. Second, we employ a fast channel search method that confines the motion graph search to a free channel with guaranteed clearance among obstacles, achieving faster and improved results that avoid expensive collision checking. Lastly, we present a motion deformation model based on Inverse Kinematics applied over the transitions of a solution branch. Each transition is assigned a continuous deformation range that does not exceed the original transition cost threshold specified by the user for the graph construction. The obtained deformation improves the reachability of the feature-based motion graph and in turn also reduces the time spent during search. The results obtained by the proposed methods are evaluated and quantified, and they demonstrate significant improvements in comparison to traditional motion graph techniques.