University of North Carolina, Chapel Hill

 

 

We present a novel interactive approach to generate plausible behaviors for a large number of virtual humans, and to enable natural interaction between the real user and virtual agents. Our formulation is based on a coupled approach that combines a 2D multi-agent navigation algorithm with 3D human motion synthesis. We have integrated our formulation with commercial HMD's to allow the user to interact with the virtual agents, thus creating an immersive experience.

 

PedVR: Simulating Gaze-Based Interactions between a Real User and Virtual Crowds

We present a novel interactive approach, PedVR, to generate plausible behaviors for a large number of virtual humans, and to enable natural interaction between the real user and virtual agents. Our formulation is based on a coupled approach that combines a 2D multi-agent navigation algorithm with 3D human motion synthesis. The coupling can result in plausible movement of virtual agents and can generate gazing behaviors, which can considerably increase the believability. We have integrated our formulation with the DK-2 HMD and demonstrate the benefits of our crowd simulation algorithm over prior decoupled approaches. Our user evaluation suggests that the combination of coupled methods and gazing behavior can considerably increase the behavioral plausibility.


Narang, S., Best, A., Randhavane, T., Shapiro, A., & Manocha, D. (2016, November). PedVR: simulating gaze-based interactions between a real user and virtual crowds. In Proceedings of the 22nd ACM Conference on Virtual Reality Software and Technology (pp. 91-100). ACM.
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FbCrowd: Interactive Multi-agent Simulation with Coupled Collision Avoidance and Human Motion Synthesis


We present an interactive algorithm to generate plausible trajectories and full-body crowd simulations. Our formulation is based on a novel two-way coupling between 2D multi-agent collision avoidance and high-DOF human motion synthesis. We present a collision-free navigation algorithm that takes into account human motion and biomechanics constraints to compute smooth trajectories. Furthermore, we present a hybrid motion synthesis algorithm that seamlessly transitions between motion blending and semi-procedural locomotion, thereby balancing control and naturalness of the synthesized motion. The overall full-body crowd simulation algorithm can generate plausible motions with lower and upper body movements for multiple agents in dynamic virtual environments at interactive rates. We demonstrate its benefits over prior interactive crowd simulation algorithms.


Narang, S., Randhavane, T., Best, A., Shapiro, A., & Manocha, D. (2016). FbCrowd: Interactive Multi-agent Simulation with Coupled Collision Avoidance and Human Motion Synthesis. Technical Report, UNC Chapel Hill.
PDF   Video: (MP4, 38.1 MB)

 

F2FCrowds: Planning Agent Movements to Enable Face-to-Face Interactions


We present an approach for multi-agent navigation that facilitates face-to-face interaction in virtual crowds. We describe a model of approach behavior for virtual agents that includes a novel interaction velocity prediction (IVP) algorithm. This algorithm is combined with human body motion synthesis constraints and facial actions to improve the behavioral realism of virtual agents. We combine these techniques with full-body crowd simulation and evaluate their benefits by conducting a user study using immersive hardware. Our results indicate that such techniques enabling face-to-face interactions can improve the sense of presence felt by the user. The virtual agents using these algorithms also appear more responsive and are able to elicit more reaction from the users.


Randhavane, T., Bera, A., & Manocha, D. (2017) F2FCrowds: Planning Agent Movements to Enable Face-to-Face Interactions. Technical Report, UNC Chapel Hill.
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