Computer Animation and Virtual Worlds最新文献

Dressing animations have broad applications in film and animation, but current methods require retraining networks for new styles, which is resource-intensive. While 2D pattern parameters are used for static 3D clothing modeling and editing, applying them to control style variations in dynamic clothing deformation is challenging due to the uncertainty introduced by multiple parameters. Ensuring consistent and stable style features across time-varying deformation sequences is difficult. Therefore, we present StyleGarNet, a novel approach for style-parameter-controlled dressing animation generation and editing. We employ a conditional variational autoencoder architecture to build our network, with style parameters serving as constraint information. This allows us to learn the probabilistic distribution model of deformations under style constraints, crucial for enhancing the robust representation of styles during the deformation process. Simultaneously, considering the temporal nature of motion, we introduce a transformer layer to capture the temporal dependencies of both motion and clothing deformation, thereby enhancing the stability of clothing deformation. Ultimately, our approach enables flexible manipulation of dressing animation generation through inputting style and motion features. Evaluation results demonstrate the efficacy of our approach and show that StyleGarNet outperforms existing methods in terms of prediction speed, accuracy, and stability of deformation sequences.

Co-speech gestures generation plays a key role in the field of virtual reality interaction, synthesizing proper digital human's actions passively with speech. The generative algorithm-based method creates realistic gestures accompanying speech's rhythm and semantic content, improving the interactive experience. To match the persona of digital humans, gestures from algorithms often require additional modification before being applied to a virtual character. However, motion sequences are difficult to edit when generated from hidden motion representations. To make motion synthesis editable, the proposed method develops a two-stage controllable gesture generation pipeline for the c-speech ge-ture generating problem. In stage 1, we design a novel large language model based -K-Decoder th-t takes speech and style label as input to synthesize inverse ki-ematic s-yle control points, which are highly editable. In stage 2, we divide the motion sequence into the body or fingers part for VQ-based latent motion representation learning relatively. And a diffusion-based IK-Denoiser is proposed for'latent motion representation synthesis under the condition of control points. Compared to other representative algorithms, the proposed method gets a competitive performance of metrics such as Fréchet Gesture Distance, Beat Consistency, and Diversity. To demonstrate controllability, it provides three explicit control strategies for motion editing. With these control points, we provide a new co-speech gesture generation paradigm.

In virtual reality (VR) applications, real-time and robust 3D human pose estimation is paramount to enhance user experience, yet existing methodologies often encounter challenges such as high computational burden, occlusion sensitivity, and inadequate adaptation to complex actions. To mitigate these issues, we propose a novel 3D human pose estimation method based on a multi-agent hierarchical biomechanical priors architecture. This method achieves efficient heatmap prediction in the local feature space through a parallel agents architecture, while simultaneously integrating a hierarchical loss function and dynamic context modeling. It incorporates virtual avatars' geometric constraints into network training, thereby enhancing pose plausibility and effectively addressing occlusion and intricate actions. Moreover, it substantially improves cross-frame stability and estimation accuracy in occlusion scenarios through multiview spatiotemporal consistency optimization. Compared to existing methods, the proposed framework provides adaptability to the unique demands of virtual environments with reduced computational cost. We experimentally validate our approach on the widely used Human3.6M and MPI-INF-3DHP datasets, and further demonstrate through ablation experiments that the dynamic occlusion compensation module, which fuses multimodal perception with a spatio-temporal diffusion mechanism, significantly enhances the robustness of pose estimation under occlusion scenarios with virtual costumes.

This study presents a novel tri-manual interaction framework that enables users to control two physical hands via VR controllers and a virtual robotic arm through a hybrid brain-computer interface (32-channel EEG and eye-tracking). The virtual robotic arm, implemented as a 6-DOF industrial manipulator in Unity, is controlled through simplified BCI commands: forward/backward movement along the z-axis based on motor imagery strength, with automatic grasping triggered by sustained attention thresholds. Twenty-five participants completed 30 trials, each following a 60-s protocol with five phases: rest, target presentation, preparation, execution, and feedback. Results demonstrated that the hybrid BCI system achieved superior performance compared to EEG-only control: 8.5% improvement in task success rate, 34.5% increase in positioning accuracy, and 46.2% reduction in cognitive load. The CNN-LSTM architecture achieved 86.3% motor imagery classification accuracy. Learning effects were observed within trials, with performance plateauing after 3.8 ± 0.7 attempts. Time-frequency analysis revealed hierarchical neural coordination mechanisms underlying the tri-manual control. This research validates the feasibility of augmented manipulation in virtual environments, establishing a foundation for advanced human-robot collaboration in animation and VR applications.

Smoke simulation is a crucial element in entertainment applications, such as movies and video games. In particular, the required smoke texture varies depending on the scene, ranging from smooth textures for cigarette smoke to highly irregular ones for explosions. The texture of smoke is primarily influenced by light sources, scattering properties, and turbulence components. Light sources and scattering properties affect the appearance of smoke during rendering, influencing its color, brightness, and density. Turbulence components significantly influence the shape of smoke. While many methods have been proposed for generating turbulence components, they all require manual adjustment of turbulence parameters, which is both time-consuming and labor-intensive. To address this issue, we propose a method for easily generating the desired turbulence by estimating turbulence parameters from images. In our approach, users input images including the desired turbulence components, and optimal parameters representing those components are automatically estimated. This reduces the time and effort required for parameter adjustment, allowing the desired turbulence components to be represented more efficiently.

The expression of fine details such as fluid flowing through narrow pipes or split by thin plates poses a significant challenge in simulations involving complex boundary conditions. As a hybrid method, the material point method (MPM), which is widely used for simulating various materials, combines the advantages of Lagrangian particles and Eulerian grids. To achieve accurate simulations of fluid flow through narrow pipes, high-resolution uniform grid cells are necessary, but this often leads to inefficient simulation performance. In this article, we present an adaptive boundary material point method that facilitates adaptive subdivision within regions of interest and conducts collision detection across grids of varying sizes. Within this framework, particles interact through grids of differing resolutions. To tackle the challenge of unevenly distributed subdivided particles, we propose a surface reconstruction approach grounded in the color distance field (CDF), which accurately defines the relationship between the particles and the reconstructed surface. Furthermore, we incorporate a mesh refinement technique to enrich the detail of the mesh utilized to mark the grids during subdivision. We demonstrate the effectiveness of our approach in simulating various materials and boundary conditions, and contrast it with existing methods, underscoring its distinctive advantages.

Motion capture options at a high rate, for example, kinematics of flying organisms, are limited. Inertial sensors or marker-based tracking are infeasible due to the mounting requirement. The advent of high-speed cameras being made available for commercial use has facilitated the discovery of intriguing high-rate behaviors. However, analyzing rapid kinematics in three dimensions necessitates using two or more high-speed cameras, which are specialized equipment and come at a significant expense. In this study, we utilize a conventional methodology of capturing synchronized multiple views using a solitary camera and a planar mirror. We introduce a user-friendly software package that facilitates the calibration of this economical setup, enabling the three-dimensional (3D) analysis of high-rate maneuvers and automatic tracking over long video sequences. Within ∼15 min, a single high-speed camera is calibrated and can be used to acquire 3D data. We accompany the toolbox with a detailed user guide and video tutorial (https://tinyurl.com/yckm9y6b) for ease of use. We accurately reconstruct dense tracks for multiple high-rate maneuvers, including flying insect species (dragonfly, butterfly, and housefly), spinning coin, and rotating fan blades. We believe that our affordable setup will assist in gathering high-rate natural phenomena, leading to realistic and diverse simulations.