论文标题
pliks：用于3D人体估计的伪线性逆运动求解器
PLIKS: A Pseudo-Linear Inverse Kinematic Solver for 3D Human Body Estimation
论文作者
论文摘要
我们从单个2D图像中介绍了Pliks（伪线性逆运动求解器），以重建人体的3D网格。当前技术通过非线性映射从输入图像中直接回归形状，姿势和翻译，对任何外部影响都具有最小的灵活性。我们将任务作为一个模型的优化问题。 PLIKS建立在参数SMPL模型的线性化公式上。使用PLIKS，我们可以通过2D像素一致的顶点分析重建人类模型。这使我们能够灵活地使用准确的相机校准信息。 Pliks提供了一种简单的方法来引入其他约束，例如形状和翻译。我们提出了定量评估，这些评估证实，与其他最先进的方法相比，与其他最先进的方法相比，在标准的3D人体姿势和形状基准相比，同时在新的Agora数据集中获得了12.9 mm的重建误差提高，而与其他最先进的方法相比，PLIKS具有更准确的重建，提高了10％。
We introduce PLIKS (Pseudo-Linear Inverse Kinematic Solver) for reconstruction of a 3D mesh of the human body from a single 2D image. Current techniques directly regress the shape, pose, and translation of a parametric model from an input image through a non-linear mapping with minimal flexibility to any external influences. We approach the task as a model-in-the-loop optimization problem. PLIKS is built on a linearized formulation of the parametric SMPL model. Using PLIKS, we can analytically reconstruct the human model via 2D pixel-aligned vertices. This enables us with the flexibility to use accurate camera calibration information when available. PLIKS offers an easy way to introduce additional constraints such as shape and translation. We present quantitative evaluations which confirm that PLIKS achieves more accurate reconstruction with greater than 10% improvement compared to other state-of-the-art methods with respect to the standard 3D human pose and shape benchmarks while also obtaining a reconstruction error improvement of 12.9 mm on the newer AGORA dataset.
