College of Culture and Art, Chengdu University of Information Technology, Chengdu 610225, China.
College of Communication Engineering, Chengdu University of Information Technology, Chengdu 610225, China.
Comput Intell Neurosci. 2022 Jun 3;2022:5106942. doi: 10.1155/2022/5106942. eCollection 2022.
With the rapid development of computer graphics, 3D animation has been applied to all fields of people's lives, especially in the industries of film and television works, games, and entertainment. The wide application of animation technology makes it difficult for general 3D animation effects to impress increasingly discerning audiences. Group animation, as a new focus, has received more and more attention and has become a hot issue in computer graphics. Traditional animation production mainly relies on manual drawing and key frame technologies. The limitations of these technologies make the production of group animation consume a lot of manpower, financial resources, and time, and cannot guarantee the intelligence of characters and the authenticity of group behavior. Therefore, in order to end the above issues, this paper proposes an animation model generation method based on Gaussian mutation genetic algorithm to optimize neural network, including obtaining animation scene data, according to the animation scene data, and extracting animation model elements. The elements are input into the model network, the target animation model is generated, and the target animation model is displayed. The method proposed in this paper improves the animation model generation method in the prior art to a certain extent. The proposed animation model is constructed only through fixed rules, and the composition rules of the model cannot be changed according to the historical data of the animation model construction and other factors. Technical issues that reduce the flexibility and accuracy of the animation model generation.
随着计算机图形学的飞速发展，3D 动画已经应用于人们生活的各个领域，尤其是在影视作品、游戏和娱乐等行业。动画技术的广泛应用使得普通的 3D 动画效果难以打动越来越挑剔的观众。群体动画作为一个新的焦点，受到了越来越多的关注，成为计算机图形学中的一个热点问题。传统的动画制作主要依赖于手工绘制和关键帧技术。这些技术的局限性使得群体动画的制作耗费大量的人力、财力和时间，并且不能保证角色的智能和群体行为的真实性。因此，为了解决上述问题，本文提出了一种基于高斯变异遗传算法优化神经网络的动画模型生成方法，包括获取动画场景数据，根据动画场景数据提取动画模型元素。将元素输入模型网络，生成目标动画模型，并显示目标动画模型。本文提出的方法在一定程度上改进了现有技术中的动画模型生成方法。所提出的动画模型仅通过固定规则构建，并且模型的组成规则不能根据动画模型构建的历史数据和其他因素进行更改。这些技术问题降低了动画模型生成的灵活性和准确性。
