| 基于单幅图像的真实感人体动画合成 |
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| 引用本文： | 徐家乐,肖学中. 基于单幅图像的真实感人体动画合成[J]. 计算机工程与应用, 2022, 58(22): 229-238. DOI: 10.3778/j.issn.1002-8331.2104-0305 |
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| 作者姓名： | 徐家乐 肖学中 |
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| 作者单位： | 南京邮电大学 计算机学院、软件学院、网络空间安全学院，南京 210003 |
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| 摘 要： | 由于单幅图像缺失三维信息以及完整的纹理信息，基于单幅图像的真实感三维人体动画合成极具挑战性。针对单幅图像三维信息缺失问题，提出了一种基于SMPL参数模型的三维人体几何重建方法。该方法以单幅图像为输入，先根据输入图像人体轮廓信息变形标准的SMPL参数模型分别生成与目标轮廓一致的正反面的三维几何模型，然后利用基于B样条插值的网格拼接融合算法拼接正反面三维几何，最后为了恢复正确的手部几何，利用基于B样条插值的网格拼接融合算法，将重建后的模型上错误的手部几何用标准SMPL参数模型上正确的手部几何替换。同时，针对单幅图像中纹理缺失的问题，提出了一个称为FBN（front to back network）的对抗生成网络，用于恢复被遮挡的人体背面纹理。实验结果表明，该方法生成的具有完整纹理的人体几何能够由3D运动数据驱动运动，生成具有高度真实感的三维人体动画。
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| 关 键 词： | 人体动画生成 三维人体重建 全身纹理生成 网格融合 深度学习 |
Photorealistic Human Body Animation Synthesis Based on Single Image |
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| XU Jiale,XIAO Xuezhong. Photorealistic Human Body Animation Synthesis Based on Single Image[J]. Computer Engineering and Applications, 2022, 58(22): 229-238. DOI: 10.3778/j.issn.1002-8331.2104-0305 |
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| Authors: | XU Jiale XIAO Xuezhong |
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| Affiliation: | School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210003, China |
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| Abstract: | Because a single image lacks three-dimensional information and complete texture information, the synthesis of realistic three-dimensional human body animation based on a single image is extremely challenging. Aiming at the problem of the lack of 3D information in a single image, a 3D human body geometric reconstruction method based on the SMPL parameter model is proposed. This method takes a single image as input, first generates a three-dimensional geometric model of the front and back surfaces that are consistent with the target contour according to the SMPL parameter model of the human body contour information deformation standard of the input image, then uses the mesh splice fusion algorithm based on B-spline interpolation to splice the front and back 3D geometry, and finally in order to restore the correct hand geometry, uses the mesh stitching fusion algorithm based on B-spline interpolation to correct the wrong hand geometry on the reconstructed model with the correct hand geometry on the standard SMPL parameter model. At the same time, in response to the problem of texture loss in a single image, a confrontation generation network called FBN（front to back network） is proposed to restore the occluded back texture of the human body. Experimental results show that the human body geometry with complete texture generated by this method can be driven by 3D motion data to generate a highly realistic three-dimensional human body animation. |
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| Keywords: | human body animation generation 3D human body reconstruction whole body texture generation mesh fusion deep learning |
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