We present a novel method to generate human motion to populate 3D indoor
scenes. It can be controlled with various combinations of conditioning signals
such as a path in a scene, target poses, past motions, and scenes represented
as 3D point clouds. State-of-the-art methods are either models specialized to
one single setting, require vast amounts of high-quality and diverse training
data, or are unconditional models that do not integrate scene or other
contextual information. As a consequence, they have limited applicability and
rely on costly training data. To address these limitations, we propose a new
method ,dubbed Purposer, based on neural discrete representation learning. Our
model is capable of exploiting, in a flexible manner, different types of
information already present in open access large-scale datasets such as AMASS.
First, we encode unconditional human motion into a discrete latent space.
Second, an autoregressive generative model, conditioned with key contextual
information, either with prompting or additive tokens, and trained for
next-step prediction in this space, synthesizes sequences of latent indices. We
further design a novel conditioning block to handle future conditioning
information in such a causal model by using a network with two branches to
compute separate stacks of features. In this manner, Purposer can generate
realistic motion sequences in diverse test scenes. Through exhaustive
evaluation, we demonstrate that our multi-contextual solution outperforms
existing specialized approaches for specific contextual information, both in
terms of quality and diversity. Our model is trained with short sequences, but
a byproduct of being able to use various conditioning signals is that at test
time different combinations can be used to chain short sequences together and
generate long motions within a context scene.

我们提出了一种新的方法，基于神经离散表示学习，用于生成人类运动以填充 3D 室内场景，并利用场景、上下文信息等多种组合的调控信号进行控制，解决了现有方法的局限性，使得模型在质量和多样性上优于现有的特定上下文信息的方法。