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.

Pose-NDF 是一种基于神经距离场的连续模型，用于生成逼真的人体姿势，学习了合理的姿势流形作为神经隐式函数的零水平集。它在各种下游任务中的表现优于现有的先进方法，例如去噪、姿势恢复和 3D 姿势重建等。

Pose-NDF: 用神经距离场建模人体姿态流形

Pose-NDF: Modeling Human Pose Manifolds with Neural Distance Fields

In this work we target a learnable output representation that allows
continuous, high resolution outputs of arbitrary shape. Recent works represent
3D surfaces implicitly with a Neural Network, thereby breaking previous
barriers in resolution, and ability to represent diverse topologies. However,
neural implicit representations are limited to closed surfaces, which divide
the space into inside and outside. Many real world objects such as walls of a
scene scanned by a sensor, clothing, or a car with inner structures are not
closed. This constitutes a significant barrier, in terms of data pre-processing
(objects need to be artificially closed creating artifacts), and the ability to
output open surfaces. In this work, we propose Neural Distance Fields (NDF), a
neural network based model which predicts the unsigned distance field for
arbitrary 3D shapes given sparse point clouds. NDF represent surfaces at high
resolutions as prior implicit models, but do not require closed surface data,
and significantly broaden the class of representable shapes in the output. NDF
allow to extract the surface as very dense point clouds and as meshes. We also
show that NDF allow for surface normal calculation and can be rendered using a
slight modification of sphere tracing. We find NDF can be used for multi-target
regression (multiple outputs for one input) with techniques that have been
exclusively used for rendering in graphics. Experiments on ShapeNet show that
NDF, while simple, is the state-of-the art, and allows to reconstruct shapes
with inner structures, such as the chairs inside a bus. Notably, we show that
NDF are not restricted to 3D shapes, and can approximate more general open
surfaces such as curves, manifolds, and functions. Code is available for
research at this https URL

本论文提出了一种名为神经距离场（Neural Distance Fields）的神经网络模型，可用于预测稀疏点云的非闭合曲面的符号距离场，以重构有内部结构的物体的表面，并实现了面法线计算和渲染。