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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. 

In this paper we propose a scalable framework for large-scale farm scene modeling that utilizes remote sensing data, specifically satellite images. Our approach begins by accurately extracting and categorizing the distributions of various scene elements from satellite images into four distinct layers: fields, trees, roads, and grasslands. For each layer, we introduce a set of controllable Parametric Layout Models (PLMs). These models are capable of learning layout parameters from satellite images, enabling them to generate complex, large-scale farm scenes that closely reproduce reality across multiple scales. Additionally, our framework provides intuitive control for users to adjust layout parameters to simulate different stages of crop growth and planting patterns. This adaptability makes our model an excellent tool for graphics and virtual reality applications. Experimental results demonstrate that our approach can rapidly generate a variety of realistic and highly detailed farm scenes with minimal inputs. 

In recent years, the field of crowd simulation has experienced significant advancements, attributed in part to the improvement of hardware performance, coupled with a notable emphasis on agent-based characteristics. Agent-based simulations stand out as the preferred methodology when researchers seek to model agents with unique behavioral traits and purpose-driven actions, a crucial aspect for simulating diverse and realistic crowd movements. This survey adopts a systematic approach, meticulously delving into the array of factors vital for simulating a heterogeneous microscopic crowd. The emphasis is placed on scrutinizing low-level behavioral details and individual features of virtual agents to capture a nuanced understanding of their interactions. The survey is based on studies published in reputable peer-reviewed journals and conferences. The primary aim of this survey is to present the diverse advancements in the realm of agent-based crowd simulations, with a specific emphasis on the various aspects of agent behavior that researchers take into account when developing crowd simulation models. Additionally, the survey suggests future research directions with the objective of developing new applications that focus on achieving more realistic and efficient crowd simulations. 

Color composition (or color theme) is a key factor to determine how well a piece of art work or graphical design is perceived by humans. Despite a few color harmony models have been proposed, their results are often less satisfactory since they mostly neglect the variations of aesthetic cognition among individuals and treat the influence of all ratings equally as if they were all rated by the same anonymous user. To overcome this issue, in this article we propose a new color theme evaluation model by combining a back propagation neural network and a kernel probabilistic model to infer both the color theme rating and the user aesthetic preference. Our experiment results show that our model can predict more accurate and personalized color theme ratings than state of the art methods. Our work is also the first-of-its-kind effort to quantitatively evaluate the correlation between user aesthetic preferences and color harmonies of five-color themes, and study such a relation for users with different aesthetic cognition. 

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. 

This paper presents a computational study to analyze and predict turns (i.e., turn-taking and turn-keeping) in multiparty conversations. Specifically, we use a high-fidelity hybrid data acquisition system to capture a large-scale set of multi-modal natural conversational behaviors of interlocutors in three-party conversations, including gazes, head movements, body movements, speech, etc. Based on the inter-pausal units (IPUs) extracted from the in-house acquired dataset, we propose a transformer-based computational model to predict the turns based on the interlocutor states (speaking/back-channeling/silence) and the gaze targets. Our model can robustly achieve more than 80% accuracy, and the generalizability of our model was extensively validated through cross-group experiments. Also, we introduce a novel computational metric called “relative engagement level" (REL) of IPUs, and further validate its statistical significance between turn-keeping IPUs and turn-taking IPUs, and between different conversational groups. Our experimental results also found that the patterns of the interlocutor states can be used as a more effective cue than their gaze behaviors for predicting turns in multiparty conversations.