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arXiv · 2405.15939

Diversifying Human Pose in Synthetic Data for Aerial-view Human Detection

Yi-Ting Shen·Hyungtae Lee·Heesung Kwon·Shuvra S. Bhattacharyya
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Abstract

Synthetic data generation has emerged as a promising solution to the data scarcity issue in aerial-view human detection. However, creating datasets that accurately reflect varying real-world human appearances, particularly diverse poses, remains challenging and labor-intensive. To address this, we propose SynPoseDiv, a novel framework that diversifies human poses within existing synthetic datasets. SynPoseDiv tackles two key challenges: generating realistic, diverse 3D human poses using a diffusion-based pose generator, and producing images of virtual characters in novel poses through a source-to-target image translator. The framework incrementally transitions characters into new poses using optimized pose sequences identified via Dijkstra's algorithm. Experiments demonstrate that SynPoseDiv significantly improves detection accuracy across multiple aerial-view human detection benchmarks, especially in low-shot scenarios, and remains effective regardless of the training approach or dataset size.

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Yi-Ting Shen, Hyungtae Lee, Heesung Kwon, Shuvra S. Bhattacharyya. 2024-05-24. Diversifying Human Pose in Synthetic Data for Aerial-view Human Detection. https://arxiv.org/abs/2405.15939

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