论文标题
人体模型拟合通过学习的梯度下降
Human Body Model Fitting by Learned Gradient Descent
论文作者
论文摘要
我们提出了一种新型算法，用于将3D人类形状拟合到图像。将基于迭代梯度的优化技术的准确性和完善能力与深神经网络的鲁棒性相结合，我们提出了一种利用神经网络来预测每种迭代的参数更新规则的梯度下降算法。这种每参数和州感知的更新将优化器引导为一个好的解决方案，以几个步骤，通常在几个步骤中融合。在训练过程中，我们的方法仅需要通过SMPL参数进行人类姿势的MOCAP数据。从这些数据中，网络了解有效姿势和形状的子空间，在该子空间中，优化的执行效率要高得多。该方法不需要任何难以获取图像到3D对应关系。在测试时，我们仅优化2D关节重新投射误差，而无需任何其他先验或正则化项。我们从经验上表明，该算法是快速（120ms收敛），稳健的初始化和数据集，并在公共评估数据集上取得了最新的结果，包括具有挑战性的3DPW内贴基准（包括Smplifie 45％的改进），并使用Image-3D对应关系进行方法
We propose a novel algorithm for the fitting of 3D human shape to images. Combining the accuracy and refinement capabilities of iterative gradient-based optimization techniques with the robustness of deep neural networks, we propose a gradient descent algorithm that leverages a neural network to predict the parameter update rule for each iteration. This per-parameter and state-aware update guides the optimizer towards a good solution in very few steps, converging in typically few steps. During training our approach only requires MoCap data of human poses, parametrized via SMPL. From this data the network learns a subspace of valid poses and shapes in which optimization is performed much more efficiently. The approach does not require any hard to acquire image-to-3D correspondences. At test time we only optimize the 2D joint re-projection error without the need for any further priors or regularization terms. We show empirically that this algorithm is fast (avg. 120ms convergence), robust to initialization and dataset, and achieves state-of-the-art results on public evaluation datasets including the challenging 3DPW in-the-wild benchmark (improvement over SMPLify 45%) and also approaches using image-to-3D correspondences
