Featured projects (1)
A project investigating embodied online dancing and partying supported by Extended Reality techniques and AI powered digital characters. The Carousel project investigates novel, original and imaginative combinations of Artificial Intelligence and immersive interaction technologies, to allow people to feel each other’s presence, touch, and movement, even if they are not in the same physical space. The project final goal is to help people to increase happiness by combating loneliness. More details are available on our website : https://www.carouseldancing.org This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 101017779. Details on the support of the European Union are provided here : https://cordis.europa.eu/project/id/101017779
Featured research (2)
Multiple importance sampling (MIS) is an indispensable tool in light-transport simulation. It enables robust Monte Carlo integration by combining samples from several techniques. However, it is well understood that such a combination is not always more efficient than using a single sampling technique. Thus a major criticism of complex combined estimators, such as bidirectional path tracing, is that they can be significantly less efficient on common scenes than simpler algorithms like forward path tracing. We propose a general method to improve MIS efficiency: By cheaply estimating the efficiencies of various technique and sample-count combinations, we can pick the best one. The key ingredient is a numerically robust and efficient scheme that uses the samples of one MIS combination to compute the efficiency of multiple other combinations. For example, we can run forward path tracing and use its samples to decide which subset of VCM to enable, and at what sampling rates. The sample count for each technique can be controlled per-pixel or globally. Applied to VCM, our approach enables robust rendering of complex scenes with caustics, without compromising efficiency on simpler scenes.
The prediction of pedestrian behavior remains a major objective for the development of autonomous vehicles. Pedestrians do not merely represent the most vulnerable traffic participants, but are also a challenge in the prediction process, since their behavior entails a large number of options for possible paths, velocities, and motions. In addition, autonomous vehicles should be able to operate safely in different countries, and thus the incorporation of cultural differences in the training and evaluation of the relevant AI systems is required. This paper provides the first review of Japanese and German pedestrians’ behavior in urban traffic. In particular, cultural behavior differences of pedestrians in risk avoidance, compliance, gap acceptance, and walking velocity together with different environmental factors like pedestrian facilities in both countries are addressed.