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We introduce a configurable framework to generate synthetic datasets featuring human characters for the training of neural networks. We demonstrate how our framework can generate a vast amount of annotated images showing a human and its hands in a multitude of poses and with varying backgrounds. Furthermore, we conduct a specific formation of synthetic hand images to train convolutional neural networks for detecting and/or segmenting hands in real scenarios in a novel way. The generated dataset aims to enhance the previously successful method of exploiting the invariancy concept with slightly more expensive rendering techniques. That is, we keep the number of subjects, poses, scenes, and other costly factors at the minimum level while increasing the diversity of the data. Our dataset features ten different human characters in various poses captured from a multitude of camera angles. We make our framework open source and publish the dataset consisting of 90,000 images.

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A Novel Framework for the Generation of Synthetic Datasets with Applications to Hand Detection and Segmentation

  • 章节

  • 出版模式：

    开放获取

  • 发表日期：

    2026年

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  • Tom Uhlmann,
  • Amin Dadgar,
  • Felix Weigand,
  • Guido Brunnett

摘要

We introduce a configurable framework to generate synthetic datasets featuring human characters for the training of neural networks. We demonstrate how our framework can generate a vast amount of annotated images showing a human and its hands in a multitude of poses and with varying backgrounds. Furthermore, we conduct a specific formation of synthetic hand images to train convolutional neural networks for detecting and/or segmenting hands in real scenarios in a novel way. The generated dataset aims to enhance the previously successful method of exploiting the invariancy concept with slightly more expensive rendering techniques. That is, we keep the number of subjects, poses, scenes, and other costly factors at the minimum level while increasing the diversity of the data. Our dataset features ten different human characters in various poses captured from a multitude of camera angles. We make our framework open source and publish the dataset consisting of 90,000 images.