论文标题
部分可观测时空混沌系统的无模型预测
Adjustable Method Based on Body Parts for Improving the Accuracy of 3D Reconstruction in Visually Important Body Parts from Silhouettes
论文作者
论文摘要
储层计算是预测湍流的有力工具，其简单的架构具有处理大型系统的计算效率。然而，其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间，然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用，该系统模拟了湍流的若干特征。我们表明，使用径向基函数作为非线性投影器，即使只有部分观测并且不知道控制方程，也能稳健地捕捉复杂的系统非线性。最后，我们表明，当测量稀疏、不完整且带有噪声，甚至控制方程变得不准确时，我们的网络仍然可以产生相当准确的预测，从而为实际湍流系统的无模型预测铺平了道路。
This research proposes a novel adjustable algorithm for reconstructing 3D body shapes from front and side silhouettes. Most recent silhouette-based approaches use a deep neural network trained by silhouettes and key points to estimate the shape parameters but cannot accurately fit the model to the body contours and consequently are struggling to cover detailed body geometry, especially in the torso. In addition, in most of these cases, body parts have the same accuracy priority, making the optimization harder and avoiding reaching the optimum possible result in essential body parts, like the torso, which is visually important in most applications, such as virtual garment fitting. In the proposed method, we can adjust the expected accuracy for each body part based on our purpose by assigning coefficients for the distance of each body part between the projected 3D body and 2D silhouettes. To measure this distance, we first recognize the correspondent body parts using body segmentation in both views. Then, we align individual body parts by 2D rigid registration and match them using pairwise matching. The objective function tries to minimize the distance cost for the individual body parts in both views based on distances and coefficients by optimizing the statistical model parameters. We also handle the slight variation in the degree of arms and limbs by matching the pose. We evaluate the proposed method with synthetic body meshes from the normalized S-SCAPE. The result shows that the algorithm can more accurately reconstruct visually important body parts with high coefficients.
