We present Pose-NDF, a continuous model for plausible human poses based on neural distance fields (NDFs). Pose or motion priors are important for generating realistic new poses and for reconstructing accurate poses from noisy or partial observations. Pose-NDF learns a manifold of plausible poses as the zero level set of a neural implicit function, extending the idea of modeling implicit surfaces in 3D to the high-dimensional domain SO(3)^K, where a human pose is defined by a single data point, represented by K quaternions. The resulting high-dimensional implicit function can be differentiated with respect to the input poses and thus can be used to project arbitrary poses onto the manifold by using gradient descent on the set of 3-dimensional hyperspheres. In contrast to previous VAE-based human pose priors, which transform the pose space into a Gaussian distribution, we model the actual pose manifold, preserving the distances between poses. We demonstrate that PoseNDF outperforms existing state-of-the-art methods as a prior in various downstream tasks, ranging from denoising real-world human mocap data, pose recovery from occluded data to 3D pose reconstruction from images. Furthermore, we show that it can be used to generate more diverse poses by random sampling and projection than VAE-based methods.
翻译:我们提出了基于神经距离场(NDFs)的貌似人造外表的连续模型Pose-NDF。 Pose-NDF对于产生现实的新外形和从噪音或局部观测重建准确的外形非常重要。 Pose-NDF学会了一组貌似合理的外形,作为神经隐含功能的零层,将3D的隐含表面建模概念扩大到高维域SO(3)QK,其中以K之四表示的单一数据点为人类外形的模型。由此产生的高维隐含功能可以在输入面上有所区别,从而可以用来通过在三维超镜集上使用梯度脱色脱色脱色脱色滑度来预测任意的外形形。与以前以VAEE为基础的人造外形前,将外形空间转化为高地分布,我们模拟了实际的外形外形外观,保持了外形之间的距离。我们证明PoseNDF在各种下游任务中,从对真实世界的外观数据进行分解到从真实的图层数据再利用到随机的图层图层图层图层图象的复原,我们可以进行更可能的图层的图象化。