《计算机应用研究》|Application Research of Computers

基于深度学习的人体动作草图到三维骨骼模型重建方法的研究

Research of deep learning based 3D skeleton model reconstruction method from human motion sketch

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作者 马昊,李淑琴,丁濛
机构 北京信息科技大学 a.计算机学院;b.感知与计算智能联合实验室,北京 100101
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文章编号 1001-3695(2020)06-055-1867-04
DOI 10.19734/j.issn.1001-3695.2018.11.0921
摘要 为了提高三维人体骨骼模型的建模效率并简化交互规则,提出了一种基于深度学习的手绘人体动作草图到三维骨骼模型的重建方法。首先将三维骨骼模型渲染为二维图像来建立维度映射关系,进而使用图像分类方法识别手绘草图动作并根据维度映射实现三维骨骼模型重建。在实验中使用基于深度卷积神经网络对图像分类模型进行构建,使用浅层卷积网络作为训练单元,并使用逐级分类与分块训练策略加速网络收敛速度来提高训练效率。最后实验结果验证了该方法的可行性与有效性。
关键词 深度学习; 卷积神经网络; 三维重建; 图像分类; 草图建模技术
基金项目 国家自然科学基金资助项目(61502039)
2017年度教育教学改革研究专项招标课题(2017JGZB08)
本文URL http://www.arocmag.com/article/01-2020-06-055.html
英文标题 Research of deep learning based 3D skeleton model reconstruction method from human motion sketch
作者英文名 Ma Hao, Li Shuqin, Ding Meng
机构英文名 a.School of Computer,b.Joint Laboratory of Sensing & Computational Intelligence,Beijing Information Science & Technology University,Beijing 100101,China
英文摘要 In order to improve the modeling efficiency of 3D human skeleton model and simplify the interaction rules, this paper presented a deep learning based 3D skeleton model reconstruction method from human motion sketch. Firstly, this method rendered the 3D skeleton models into 2D images to establish the dimension mapping and then used the image classification method to recognize motion from sketch and further to realize 3D skeleton model reconstruction according to the dimension mapping between 2D and 3D. This method built the image classification model base on CNN and used a shallow convolutional network as the training unit in the experiment. This method also used hierarchical classification and blocking training scheme to accelerate the convergence time of network to improve training efficiency. Finally, the experiment results verify the feasibility and effectiveness of this method.
英文关键词 deep learning; convolutional neural network(CNN); 3D reconstruction; image classification; sketch-based modeling technology
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收稿日期 2018/11/18
修回日期 2019/3/1
页码 1867-1870
中图分类号 TP391.41
文献标志码 A