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Perception of Motion Variations in Large-Scale Virtual Human Crowds

Robin Adili 1 Benjamin Niay 1 Katja Zibrek 1 Anne-Hélène Olivier 1 Julien Pettré 2 Ludovic Hoyet 1
1 MIMETIC - Analysis-Synthesis Approach for Virtual Human Simulation
UR2 - Université de Rennes 2, IRISA-D6 - MEDIA ET INTERACTIONS, Inria Rennes – Bretagne Atlantique
2 RAINBOW - Sensor-based and interactive robotics
Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
Abstract : Virtual human crowds are regularly featured in movies and video games. With a large number of virtual characters each behaving in their own way, spectacular scenes can be produced. The more diverse the characters and their behaviors are, the more realistic the virtual crowd is expected to be perceived. Hence, creating virtual crowds is a trade-off between the cost associated with acquiring more diverse assets, namely more virtual characters with their animations, and achieving better realism. In this paper, our focus is on the perceived variety in virtual crowd character motions. We present an experiment exploring whether observers are able to identify virtual crowds including motion clones in the case of large-scale crowds (from 250 to 1000 characters). As it is not possible to acquire individual motions for such numbers of characters, we rely on a state-of-the-art motion variation approach to synthesize unique variations of existing examples for each character in the crowd. Participants then compared pairs of videos, where each character was animated either with a unique motion or using a subset of these motions. Our results show that virtual crowds with more than two motions (one per gender) were perceptually equivalent, regardless of their size. We believe these findings can help create efficient crowd applications, and are an additional step into a broader understanding of the perception of motion variety.
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https://hal.inria.fr/hal-03468589
Contributor : Ludovic Hoyet Connect in order to contact the contributor
Submitted on : Tuesday, December 7, 2021 - 3:30:01 PM
Last modification on : Tuesday, January 4, 2022 - 6:46:34 AM

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2021_MIG_Adili.pdf
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Robin Adili, Benjamin Niay, Katja Zibrek, Anne-Hélène Olivier, Julien Pettré, et al.. Perception of Motion Variations in Large-Scale Virtual Human Crowds. MIG 2021 - 14th Annual ACM SIGGRAPH Conference on Motion, Interaction and Games, Nov 2021, Virtual Event Switzerland, France. pp.1-7, ⟨10.1145/3487983.3488288⟩. ⟨hal-03468589⟩

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