As eusocial creatures, bees display unique macro collective behavior and local body dynamics that hold potential applications 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 oscillations, 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' innate 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.

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.

Butterflies are not only ubiquitous around the world but are also widely known for inspiring thrill resonance, with their elegant and peculiar flights. However, realistically modeling and simulating butterfly flights—in particular, for real-time graphics and animation applications—remains an under-explored problem. In this article, we propose an efficient and practical model to simulate butterfly flights. We first model a butterfly with parametric maneuvering functions, including wing-abdomen interaction. Then, we simulate dynamic maneuvering control of the butterfly through our force-based model, which includes both the aerodynamics force and the vortex force. Through many simulation experiments and comparisons, we demonstrate that our method can efficiently simulate realistic butterfly flight motions in various real-world settings.

Abstract Fluid‐like motion and linear wave propagation behavior will emerge when we impose boundary constraints and polarized conditions on crowds. To this end, we present a Lagrangian hydrodynamics method to simulate the fluid‐like motion of crowd and a triggering approach to generate the linear stop‐and‐go wave behavior. Specifically, we impose a self‐propulsion force on the leading agents of the crowd to push the crowd to move forward and introduce a Smoothed Particle Hydrodynamics‐based model to simulate the dynamics of dense crowds. Besides, we present a motion signal propagation approach to trigger the rest of the crowd so that they respond to the immediate leaders linearly, which can lead to the linear stop‐and‐go wave effect of the fluid‐like motion for the crowd. Our experiments demonstrate that our model can simulate large‐scale dense crowds with linear wave propagation.

Despite numerous progresses in the past decades, 3D shape acquisition techniques remain a threshold for various 3D face based applications. Moreover, advanced 2D data generative models based on the deep networks may not be directly applicable for 3D objects. In this work, we propose a geometry sampling approach to bridge the gap between unstructured 3D face models and the powerful deep networks towards an unsupervised 3D face generative model. Specifically, we devise a geometry sampling approach to obtain a structured representation of 3D faces, which enable us to adapt the 3D faces to the Deep Convolution Generative Adversarial Network (DCGAN) for 3D face generation. We have demonstrated the effectiveness of our generative model by producing a large variety of 3D faces with different facial expressions.

Abstract During the past decades, high‐fidelity realistic simulations of various flying insects exhibiting collective behavior have been broadly used in entertainment industries and virtual reality applications. However, due to the intrinsic complexity and high computational cost, shape constrained simulation of collective behaviors remains a challenging topic. In this paper, we present a robust multi‐agent model for large‐scale controllable shape constrained simulation of flying insects. Specifically, we design an internal force model to biologically mimic an individual insect. We also propose an external force model based on a trade‐off mechanic to guide the insects smoothly deforming into a target shape. Our experimental results and comparative studies show our method is able to simulate realistic and dynamic flying insects with various user‐specified shape constraints.

Motor vehicle accidents are one of the leading causes of death. While lane departure warning, blind spot warning, and driver attention monitoring systems for avoiding collisions have been in development for quite sometime, to date mostly luxury cars are only equipped with these safety features. As a cheaper and ubiquitous alternative, we explore how a smartphone can assist an inattentive driver by leveraging its front and back cameras apart from other sensors. The challenge, however, is given the resource constraints of a smartphone, how quickly and accurately can it detect an unintended maneuver and alert the driver. In this paper, we describe our on-going attempt to address this challenge.