ABSTRACT Generating diverse and realistic movements has long been a central challenge in computer graphics. Generative Adversarial Networks (GANs) remain a compelling solution due to their ability to perform well even with limited training data. However, traditional GANs generate samples directly, which can lead to the omission of certain data patterns. To address this limitation, we introduce SinMDGan , a hybrid deep learning framework for single‐motion synthesis that leverages a Diffusion‐GAN model. Our approach integrates the strengths of GANs, which capture global motion characteristics, with diffusion techniques, which refine local details, ensuring both authenticity and diversity in generated movements. Unlike conventional cascaded GANs, our framework employs a single generator‐discriminator pair, utilizing different diffusion time steps to synthesize novel and diverse motions from a single short sequence. Experimental evaluations demonstrate the effectiveness of our model in achieving stable data distribution coverage and enhancing output diversity. Additionally, we showcase various applications, including motion composition and long‐sequence generation, highlighting the versatility of our approach.

Modeling and simulating realistic insect flight pose unique challenges due to the complex interaction between multi-degree-of-freedom wing kinematics and highly precise aerodynamic forces. To solve this challenge, this article presents a bidirectional kinematics-aerodynamics coupled simulation framework for miniature insect flight. Our approach first models the kinematics of flying insects by parameterizing natural wingbeat cycles based on available real-world datasets. Subsequently, we compute aerodynamic forces utilizing an improved semi-empirical model, which extends from quasi-steady formulation by incorporating critical unsteady force components. To achieve closed-loop control for both kinematics and aerodynamics, we employ deep reinforcement learning to train a virtual insect to adaptively adjust flapping strategies in response to dynamic flight states. Finally, an integrated controller enables the simulated insect to autonomously regulate the wing motion and perform complex tasks such as visual obstacle avoidance. Extensive experiments and comparisons demonstrate that our framework can effectively generate physically plausible and autonomous insect flight across a variety of scenarios.

In the era of big data and IoT, personalized federated learning (pFL) addresses privacy challenges by keeping training on-device and transmitting only parameter updates. This approach allows each client to incorporate their unique data characteristics, effectively tailoring the global model to diverse user needs. However, as pFL leverages personalized updates that capture individual client experiences, it faces the inherent risk of exposing sensitive information during the aggregation process. Global federated methods like FedAvg further exacerbate this issue when the non-IID assumption is violated across clients, as they struggle to maintain performance across varied data distributions, often leading to suboptimal results. To tackle this, we propose fine-grained Privacy-aware Parameter Coaching method for personalized Federated learning(PPCFed). We propose a dynamic privacy matrix that quantifies layer-wise privacy leakage risks and adaptively reweights inter-client knowledge transfer. This matrix acts as a fine-grained privacy-aware controller, enabling clients to selectively assimilate insights from others without additional overhead while actively suppressing high-risk information flows. Our method balances privacy and performance, showing improved results in heterogeneous FL and pFL environments.

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.

Abstract Nowadays, virtual reality (VR) is becoming an important technique for various educational subjects. However, Chinese calligraphy, as a unique artistic form, remains under-explored in terms of learning in a VR configuration. This deficiency is largely due to the challenge to render delicate haptic feedback of pen and brush during the process of writing. To achieve the purpose of haptic rendering, existing works mostly use the professional device (e.g. Phantom), which is expensive and not accessible to common users. Our work presents a novel yet simple approach to render haptic feedback for Chinese calligraphy in VR by using soft and deformable sponge as the medium between the handheld controller and writing surface. We compared three different feedback configurations using on-device vibration and sponge-enabled haptic feedback against the baseline configuration with no force feedback. Based on both the qualitative and quantitative results from user studies, we found that sponge-based haptic feedback not only provided a comfort experience of interactive virtual writing but also accelerated the learning performance of novices. Our approach is low cost, scalable and produces realistic user experience, which offers an alternative solution for future development of training systems for virtual Chinese calligraphy.

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.