Computer Animation and Virtual Worlds最新文献

Crowd evacuation has gained increasing attention in recent years. The agent-based method has shown a superior capability to simulate complex behaviors during crowd evacuation simulation. For agent modeling, most existing methods only consider the decision process but ignore the detailed physical motion. In this article, we propose a hierarchical framework for crowd evacuation simulation, which combines the agent decision model with the agent motion model. In the decision model, we integrate emotional contagion and scene information to determine global path planning and local collision avoidance. In the motion model, we introduce a physics-based character control method and control agent motion using deep reinforcement learning. Based on the decision strategy, the decision model can use a signal to control the agent motion in the motion model. Compared with existing methods, our framework can simulate physical interactions between agents and the environment. The results of the crowd evacuation simulation demonstrate that our framework can simulate crowd evacuation with physical fidelity.

Debris flow is a highly destructive natural disaster, necessitating accurate simulation and prediction. Existing simulation methods tend to be overly simplified, neglecting the three-dimensional complexity and multiphase fluid interactions, and they also lack comprehensive consideration of soil conditions. We propose a novel two-particle debris flow simulation method based on smoothed particle hydrodynamics (SPH) for enhanced accuracy. Our method employs a sophisticated two-particle model coupling debris flow dynamics with SPH to simulate fluid-solid interaction effectively, which considers various soil factors, dividing terrain into variable and fixed areas, incorporating soil impact factors for realistic simulation. By dynamically updating positions and reconstructing surfaces, and employing GPU and hash lookup acceleration methods, we achieve accurate simulation with significantly efficiency. Experimental results validate the effectiveness of our method across different conditions, making it valuable for debris flow risk assessment in natural disaster management.

Trajectory prediction is essential for intelligent autonomous systems like autonomous driving, behavior analysis, and service robotics. Deep learning has emerged as the predominant technique due to its superior modeling capability for trajectory data. However, deep learning-based models face challenges in effectively utilizing scene information and accurately modeling agent interactions, largely due to the complexity and uncertainty of real-world scenarios. To mitigate these challenges, this study presents a novel multiagent trajectory prediction model, termed the global-local scene-enhanced social interaction graph network (GLSESIGN), which incorporates two pivotal strategies: global-local scene information utilization and a social adaptive attention graph network. The model hierarchically learns scene information relevant to multiple intelligent agents, thereby enhancing the understanding of complex scenes. Additionally, it adaptively captures social interactions, improving adaptability to diverse interaction patterns through sparse graph structures. This model not only improves the understanding of complex scenes but also accurately predicts future trajectories of multiple intelligent agents by flexibly modeling intricate interactions. Experimental validation on public datasets substantiates the efficacy of the proposed model. This research offers a novel model to address the complexity and uncertainty in multiagent trajectory prediction, providing more accurate predictive support in practical application scenarios.

Specular highlights detection and removal is a challenging task. Although various methods exist for removing specular highlights, they often fail to effectively preserve the color and texture details of objects after highlight removal due to the high brightness and nonuniform distribution characteristics of highlights. Furthermore, when processing scenes with complex highlight properties, existing methods frequently encounter performance bottlenecks, which restrict their applicability. Therefore, we introduce a highlight mask-guided adaptive residual network (HMGARN). HMGARN comprises three main components: detection-net, adaptive-removal network (AR-Net), and reconstruct-net. Specifically, detection-net can accurately predict highlight mask from a single RGB image. The predicted highlight mask is then inputted into the AR-Net, which adaptively guides the model to remove specular highlights and estimate an image without specular highlights. Subsequently, reconstruct-net is used to progressively refine this result, remove any residual specular highlights, and construct the final high-quality image without specular highlights. We evaluated our method on the public dataset (SHIQ) and confirmed its superiority through comparative experimental results.

Face reenactment technology is widely applied in various applications. However, the reconstruction effects of existing methods are often not quite realistic enough. Thus, this paper proposes a progressive face reenactment method. First, to make full use of the key information, we propose adaptive convolution and instance normalization to encode the key information into all learnable parameters in the network, including the weights of the convolution kernels and the means and variances in the normalization layer. Second, we present continuous transitive facial expression generation according to all the weights of the network generated by the key points, resulting in the continuous change of the image generated by the network. Third, in contrast to classical convolution, we apply the combination of depth- and point-wise convolutions, which can greatly reduce the number of weights and improve the efficiency of training. Finally, we extend the proposed face reenactment method to the face editing application. Comprehensive experiments demonstrate the effectiveness of the proposed method, which can generate a clearer and more realistic face from any person and is more generic and applicable than other methods.

To monitor and assess social dynamics and risks at large gatherings, we propose “SocialVis,” a comprehensive monitoring system based on multi-object tracking and graph analysis techniques. Our SocialVis includes a camera detection system that operates in two modes: a real-time mode, which enables participants to track and identify close contacts instantly, and an offline mode that allows for more comprehensive post-event analysis. The dual functionality not only aids in preventing mass gatherings or overcrowding by enabling the issuance of alerts and recommendations to organizers, but also allows for the generation of proximity-based graphs that map participant interactions, thereby enhancing the understanding of social dynamics and identifying potential high-risk areas. It also provides tools for analyzing pedestrian flow statistics and visualizing paths, offering valuable insights into crowd density and interaction patterns. To enhance system performance, we designed the SocialDetect algorithm in conjunction with the BYTE tracking algorithm. This combination is specifically engineered to improve detection accuracy and minimize ID switches among tracked objects, leveraging the strengths of both algorithms. Experiments on both public and real-world datasets validate that our SocialVis outperforms existing methods, showing $��1�.�2�\%�$ improvement in detection accuracy and $��45�.�4�\%�$ reduction in ID switches in dense pedestrian scenarios.

Human activity recognition (HAR) has significant potential in virtual sports applications. However, current HAR networks often prioritize high accuracy at the expense of practical application requirements, resulting in networks with large parameter counts and computational complexity. This can pose challenges for real-time and efficient recognition. This paper proposes a hybrid lightweight DSANet network designed to address the challenges of real-time performance and algorithmic complexity. The network utilizes a multi-scale depthwise separable convolutional (Multi-scale DWCNN) module to extract spatial information and a multi-layer Gated Recurrent Unit (Multi-layer GRU) module for temporal feature extraction. It also incorporates an improved channel-space attention module called RCSFA to enhance feature extraction capability. By leveraging channel, spatial, and temporal information, the network achieves a low number of parameters with high accuracy. Experimental evaluations on UCIHAR, WISDM, and PAMAP2 datasets demonstrate that the network not only reduces parameter counts but also achieves accuracy rates of 97.55%, 98.99%, and 98.67%, respectively, compared to state-of-the-art networks. This research provides valuable insights for the virtual sports field and presents a novel network for real-time activity recognition deployment in embedded devices.

Makeup in real life varies widely and is personalized, presenting a key challenge in makeup transfer. Most previous makeup transfer techniques divide the face into distinct regions for color transfer, frequently neglecting details like eyeshadow and facial contours. Given the successful advancements of Transformers in various visual tasks, we believe that this technology holds large potential in addressing pose, expression, and occlusion differences. To explore this, we propose novel pipeline which combines well-designed Convolutional Neural Network with Transformer to leverage the advantages of both networks for high-quality facial makeup transfer. This enables hierarchical extraction of both local and global facial features, facilitating the encoding of facial attributes into pyramid feature maps. Furthermore, a Low-Frequency Information Fusion Module is proposed to address the problem of large pose and expression variations which exist between the source and reference faces by extracting makeup features from the reference and adapting them to the source. Experiments demonstrate that our method produces makeup faces that are visually more detailed and realistic, yielding superior results.

This paper proposes a novel method that enhances the optimization-based elastic body solver. The proposed method tackles the element inversion problem, which is prevalent in the prediction-projection approach for numerical simulation of elastic bodies. At the prediction stage, our method alleviates inversions such that the subsequent projection solver can benefit in stability and efficiency. To prevent excessive suppression of predicted inertial motion when alleviating, we introduce a velocity decomposition method and adapt only the non-rigid motion while preserving the rigid motion, that is, linear and angular momenta. Thanks to the respected inertial motion in the prediction stage, our method produces lively motions while keeping the entire simulation more stable. The experiments demonstrate that our alleviation method successfully stabilizes the simulation and improves the efficiency particularly when large deformations hamper the solver.