Search for: All records

As eusocial creatures, bees display unique macro col- lective behavior and local body dynamics that hold potential ap- plications in various fields, such as computer animation, robotics, and social behavior. Unlike birds and fish, bees fly in a low-aligned zigzag pattern. Additionally, bees rely on visual signals for foraging and predator avoidance, exhibiting distinctive local body oscilla- tions, such as body lifting, thrusting, and swaying. These inherent features pose significant challenges to realistic bee simulations in practical animation applications. In this article, we present a bio-inspired model for bee simulations capable of replicating both macro collective behavior and local body dynamics of bees. Our approach utilizes a visually-driven system to simulate a bee’s local body dynamics, incorporating obstacle perception and body rolling control for effective collision avoidance. Moreover, we develop an oscillation rule that captures the dynamics of the bee’s local bodies, drawing on insights from biological research. Our model extends beyond simulating individual bees’ dynamics; it can also represent bee swarms by integrating a fluid-based field with the bees’ in- nate noise and zigzag motions. To fine-tune our model, we utilize pre-collected honeybee flight data. Through extensive simulations and comparative experiments, we demonstrate that our model can efficiently generate realistic low-aligned and inherently noisy bee swarms. 

Realistic simulation of the intricate wing deformations seen in flying insects not only deepens our comprehension of insect fight mechanics but also opens up numerous applications in fields such as computer animation and virtual reality. Despite its importance, this research area has been relatively under-explored due to the complex and diverse wing structures and the intricate patterns of deformation. This paper presents an efficient skeleton-driven model specifically designed to real-time simulate realistic wing deformations across a wide range of flying insects. Our approach begins with the construction of a virtual skeleton that accurately reflects the distinct morphological characteristics of individual insect species. This skeleton serves as the foundation for the simulation of the intricate deformation wave propagation often observed in wing deformations. To faithfully reproduce the bending effect seen in these deformations, we introduce both internal and external forces that act on the wing joints, drawing on periodic wing-beat motion and a simplified aerodynamics model. Additionally, we utilize mass- spring algorithms to simulate the inherent elasticity of the wings, helping to prevent excessive twisting. Through various simulation experiments, comparisons, and user studies, we demonstrate the effectiveness, robustness, and adaptability of our model. 

Simulating realistic butterfly motion has been a widely-known challenging problem in computer animation. Arguably, one of its main reasons is the difficulty of acquiring accurate flight motion of butterflies. In this paper we propose a practical yet effective, optical marker-based approach to capture and process the detailed motion of a flying butterfly. Specifically, we first capture the trajectories of the wings and thorax of a flying butterfly using optical marker based motion tracking. After that, our method automatically fills the positions of missing markers by exploiting the continuity and relevance of neighboring frames, and improves the quality of the captured motion via noise filtering with optimized parameter settings. Through comparisons with existing motion processing methods, we demonstrate the effectiveness of our approach to obtain accurate flight motions of butterflies. Furthermore, we created and will release a first-of-its-kind butterfly motion capture dataset to research community.