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Humans are very diverse and although we share many similarities, we also have many differences. The social groups we belong to help form our identities (Tajfel, 1974). These differences may be difficult for some people to reconcile, which may lead to prejudice toward people who are different. Prejudice is a negative attitude and feeling toward an individual based solely on one’s membership in a particular social group (Allport, 1954; Brown, 2010). Prejudice is common against people who...
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视觉到触觉交叉模式生成使用具有多尺度歧视器和混合损失的类条件GAN.

Nikolay Neshov1, Krasimir Tonchev1, Agata Manolova1

  • 1Faculty of Telecommunications, Technical University of Sofia, 8 Kliment Ohridski Blvd., 1000 Sofia, Bulgaria.

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概括
此摘要是机器生成的。

本研究介绍了一个类条件的生成对抗网络 (cGAN),用于将视觉纹理信息转换为触觉谱图,以获得现实的触觉反. 这种新的方法显著提高了虚拟现实应用的跨模式翻译精度.

关键词:
在LMT-108数据集中.增强现实 (AR) 是一种增强现实.有条件的生成对抗网络 (cGAN)交叉模式的发电方式.触觉反是一种触觉反.混合损失 混合损失 混合损失多个尺度的歧视者.质感到触觉的翻译振动器的合成虚拟现实 虚拟现实 虚拟现实 虚拟现实

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科学领域:

  • 计算机视觉 计算机视觉
  • 触觉学是指触觉学是指触觉学.
  • 机器学习 机器学习

背景情况:

  • 视觉纹理感知对于触觉染和虚拟现实至关重要.
  • 将视觉纹理数据翻译为触觉反带来了重大挑战.

研究的目的:

  • 开发一个类条件的生成对抗网络 (cGAN) 来实现从纹理图像到振动感光谱的跨模转换.
  • 在翻译过程中增强纹理类语义,以提高触觉现实性.

主要方法:

  • 使用了一个适应pix2pix的生成器,具有条件批量规范化 (CBN) 和一个DenseNet-201标签预测器.
  • 采用多尺度歧视器架构 (源自pix2pixHD) 实现最佳的感知相似性.
  • 实现了混合损失函数,将对抗性,L1和特征匹配损失结合起来.

主要成果:

  • 通过学习感知图像补丁相似性 (LPIPS) 和弗雷切开始距离 (FID) 度量来实现卓越的感知相似性.
  • 与像pix2pix和pix2pixHD这样的现有模型相比,表现出更好的性能.
  • GradCAM可视化证实了类调节的有效性.

结论:

  • 拟议的cGAN模型有效地将视觉纹理信息转化为振动触觉谱图.
  • 生成的光谱图可以转换为触觉信号,从而实现先进的触觉反和虚拟材料模拟.
  • 这项工作推进了用于沉浸式虚拟体验的跨模式翻译领域.