We propose a method to estimate 3D human poses from substantially blurred images. The key idea is to tackle the inverse problem of image deblurring by modeling the forward problem with a 3D human model, a texture map, and a sequence of poses to describe human motion. The blurring process is then modeled by a temporal image aggregation step. Using a differentiable renderer, we can solve the inverse problem by backpropagating the pixel-wise reprojection error to recover the best human motion representation that explains a single or multiple input images. Since the image reconstruction loss alone is insufficient, we present additional regularization terms. To the best of our knowledge, we present the first method to tackle this problem. Our method consistently outperforms other methods on significantly blurry inputs since they lack one or multiple key functionalities that our method unifies, i.e. image deblurring with sub-frame accuracy and explicit 3D modeling of non-rigid human motion.

翻译：我们提出一种从显著模糊图像中估计三维人体姿态的方法。核心思想是通过使用三维人体模型、纹理贴图以及描述人体运动的姿态序列对前向问题进行建模，从而解决图像去模糊的反问题。模糊过程则通过时间图像聚合步骤进行建模。利用可微分渲染器，我们可以通过反向传播逐像素重投影误差来解决反问题，恢复出能够解释单张或多张输入图像的最佳人体运动表示。由于仅依靠图像重建损失不足，我们引入了额外的正则化项。据我们所知，这是首篇解决该问题的方法。我们的方法在显著模糊输入上始终优于其他方法，因为其他方法缺乏本方法统一的多项关键功能，即具有子帧精度的图像去模糊以及非刚性人体运动的显式三维建模。