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Augmenting Character Designers Creativity Using Generative Adversarial Networks 利用生成对抗网络增强角色设计师的创造力
Pub Date : 2023-05-28 DOI: 10.48550/arXiv.2305.18387
M. Lataifeh, Xavier A. Carrasco, A. Elnagar, Naveed Ahmed
Recent advances in Generative Adversarial Networks (GANs) continue to attract the attention of researchers in different fields due to the wide range of applications devised to take advantage of their key features. Most recent GANs are focused on realism, however, generating hyper-realistic output is not a priority for some domains, as in the case of this work. The generated outcomes are used here as cognitive components to augment character designers creativity while conceptualizing new characters for different multimedia projects. To select the best-suited GANs for such a creative context, we first present a comparison between different GAN architectures and their performance when trained from scratch on a new visual characters dataset using a single Graphics Processing Unit. We also explore alternative techniques, such as transfer learning and data augmentation, to overcome computational resource limitations, a challenge faced by many researchers in the domain. Additionally, mixed methods are used to evaluate the cognitive value of the generated visuals on character designers agency conceptualizing new characters. The results discussed proved highly effective for this context, as demonstrated by early adaptations to the characters design process. As an extension for this work, the presented approach will be further evaluated as a novel co-design process between humans and machines to investigate where and how the generated concepts are interacting with and influencing the design process outcome.
生成对抗网络(GANs)的最新进展继续吸引着不同领域研究人员的关注,因为广泛的应用是为了利用其关键特征而设计的。最近的gan关注的是真实感,然而,产生超现实输出并不是某些领域的优先事项,就像这项工作的情况一样。生成的结果在这里用作认知组件,以增强角色设计师的创造力,同时为不同的多媒体项目概念化新角色。为了选择最适合这种创造性背景的GAN,我们首先比较了不同GAN架构及其在使用单个图形处理单元在新的视觉字符数据集上从头开始训练时的性能。我们还探索了替代技术,如迁移学习和数据增强,以克服计算资源限制,这是该领域许多研究人员面临的挑战。此外,混合方法用于评估生成的视觉效果对角色设计师构思新角色的认知价值。讨论的结果在这种情况下是非常有效的,正如早期对角色设计过程的调整所证明的那样。作为这项工作的延伸,所提出的方法将被进一步评估为人类和机器之间的新型协同设计过程,以调查生成的概念在何处以及如何与设计过程结果相互作用并影响设计过程结果。
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引用次数: 1
Prioritized SIPP for Multi-Agent Path Finding With Kinematic Constraints 运动约束下多智能体寻径的优先SIPP算法
Pub Date : 2021-08-11 DOI: 10.1007/978-3-030-87725-5_1
Zain Alabedeen Ali, K. Yakovlev
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引用次数: 3
Modeling of Increased Rigidity of Industrial Manipulator 工业机械手增加刚度的建模
Pub Date : 2020-10-07 DOI: 10.1007/978-3-030-60337-3_17
E. Larkin, A. Bogomolov, M. Antonov
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引用次数: 0
Data Exchange Method for Wireless UAV-Aided Communication in Sensor Systems and Robotic Devices 传感器系统和机器人设备中无线无人机辅助通信的数据交换方法
Pub Date : 2020-10-07 DOI: 10.1007/978-3-030-60337-3_5
A. Denisov, A. Shabanova, O. Sivchenko
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引用次数: 3
Spatial Resolution-Independent CNN-Based Person Detection in Agricultural Image Data 基于空间分辨率无关的农业图像数据cnn人检测
Pub Date : 2020-10-07 DOI: 10.1007/978-3-030-60337-3_19
Alexander Leipnitz, T. Strutz, O. Jokisch
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引用次数: 1
Accurate Autonomous UAV Landing Using Vision-Based Detection of ArUco-Marker 基于视觉检测aruco标记的无人机精确着陆
Pub Date : 2020-10-07 DOI: 10.1007/978-3-030-60337-3_18
I. Lebedev, A. Erashov, A. Shabanova
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引用次数: 18
Fast Face Features Extraction Based on Deep Neural Networks for Mobile Robotic Platforms 基于深度神经网络的移动机器人平台快速人脸特征提取
Pub Date : 2020-10-07 DOI: 10.1007/978-3-030-60337-3_20
M. Letenkov, D. Levonevskiy
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引用次数: 2
Indoor vs. Outdoor Scene Classification for Mobile Robots 移动机器人的室内与室外场景分类
Pub Date : 2020-10-07 DOI: 10.1007/978-3-030-60337-3_24
P. Neduchal, Ivan Gruber, M. Železný
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引用次数: 2
Humanoid Robot Soccer Player for RoboCup Junior League Competitions 机器人世界杯青少年联赛人形机器人足球运动员
Pub Date : 2020-10-07 DOI: 10.1007/978-3-030-60337-3_28
Evgeny Shandarov, I. Shabalin, Irina Prokazina, V. Zhelonkin, E. Polyntsev, Alina Sogomonyants
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引用次数: 0
Energy-Efficient Path Planning Algorithm on Three-Dimensional Large-Scale Terrain Maps for Mobile Robots 移动机器人三维大比例尺地形图的节能路径规划算法
Pub Date : 2020-10-07 DOI: 10.1007/978-3-030-60337-3_31
K. Zakharov, A. Saveliev, O. Sivchenko
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引用次数: 13
期刊
International Conference on Interactive Collaborative Robotics
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