The synthesis of human grasping has numerous applications including AR/VR,
video games and robotics. While methods have been proposed to generate
realistic hand-object interaction for object grasping and manipulation, these
typically only consider interacting hand alone. Our goal is to synthesize
whole-body grasping motions. Starting from an arbitrary initial pose, we aim to
generate diverse and natural whole-body human motions to approach and grasp a
target object in 3D space. This task is challenging as it requires modeling
both whole-body dynamics and dexterous finger movements. To this end, we
propose SAGA (StochAstic whole-body Grasping with contAct), a framework which
consists of two key components: (a) Static whole-body grasping pose generation.
Specifically, we propose a multi-task generative model, to jointly learn static
whole-body grasping poses and human-object contacts. (b) Grasping motion
infilling. Given an initial pose and the generated whole-body grasping pose as
the start and end of the motion respectively, we design a novel contact-aware
generative motion infilling module to generate a diverse set of grasp-oriented
motions. We demonstrate the effectiveness of our method, which is a novel
generative framework to synthesize realistic and expressive whole-body motions
that approach and grasp randomly placed unseen objects. Code and models are
available at this https URL

提出了一种名为 SAGA 的新型的人体抓握生成框架，该框架以多任务生成模型为核心，实现了整体抓握姿态和人 - 物接触点的联合学习，并设计了接触感知的生成运动插值模块来生成多样的姿态运动。

SAGA: 随机全身接触抓取

SAGA: Stochastic Whole-Body Grasping with Contact

In this paper we propose a convolutional autoencoder to address the problem
of motion infilling for 3D human motion data. Given a start and end sequence,
motion infilling aims to complete the missing gap in between, such that the
filled in poses plausibly forecast the start sequence and naturally transition
into the end sequence. To this end, we propose a single, end-to-end trainable
convolutional autoencoder. We show that a single model can be used to create
natural transitions between different types of activities. Furthermore, our
method is not only able to fill in entire missing frames, but it can also be
used to complete gaps where partial poses are available (e.g. from end
effectors), or to clean up other forms of noise (e.g. Gaussian). Also, the
model can fill in an arbitrary number of gaps that potentially vary in length.
In addition, no further post-processing on the model's outputs is necessary
such as smoothing or closing discontinuities at the end of the gap. At the
heart of our approach lies the idea to cast motion infilling as an inpainting
problem and to train a convolutional de-noising autoencoder on image-like
representations of motion sequences. At training time, blocks of columns are
removed from such images and we ask the model to fill in the gaps. We
demonstrate the versatility of the approach via a number of complex motion
sequences and report on thorough evaluations performed to better understand the
capabilities and limitations of the proposed approach.

本论文提出一种基于卷积自编码器的算法来解决 3D 人体运动数据中的运动填充问题，通过将缺失的姿势预测出来以使得填充后的姿势能够自然地过渡到结束序列。此方法不仅能够填补完整的帧，还能够用于补充部分姿势和去除噪声，同时能够自动拟合变化长度的缺口。