"Human-aware" has become a popular keyword used to describe a particular
class of AI systems that are designed to work and interact with humans. While
there exists a surprising level of consistency among the works that use the
label human-aware, the term itself mostly remains poorly understood. In this
work, we retroactively try to provide an account of what constitutes a
human-aware AI system. We see that human-aware AI is a design-oriented
paradigm, one that focuses on the need for modeling the humans it may interact
with. Additionally, we see that this paradigm offers us intuitive dimensions to
understand and categorize the kinds of interactions these systems might have
with humans. We show the pedagogical value of these dimensions by using them as
a tool to understand and review the current landscape of work related to
human-AI systems that purport some form of human modeling. To fit the scope of
a workshop paper, we specifically narrowed our review to papers that deal with
sequential decision-making and were published in a major AI conference in the
last three years. Our analysis helps identify the space of potential research
problems that are currently being overlooked. We perform additional analysis on
the degree to which these works make explicit reference to results from social
science and whether they actually perform user-studies to validate their
systems. We also provide an accounting of the various AI methods used by these
works.

人机感知人工智能是一种以设计为导向的范式，专注于对其可能与之互动的人进行建模，并通过使用这些维度作为工具，了解和审查与人工智能系统相关的当前工作情况。

序贯决策中的人类建模：通过人类感知人工智能之镜透析

Human-Modeling in Sequential Decision-Making: An Analysis through the  Lens of Human-Aware AI

The appearance of a human in clothing is driven not only by the pose but also
by its temporal context, i.e., motion. However, such context has been largely
neglected by existing monocular human modeling methods whose neural networks
often struggle to learn a video of a person with large dynamics due to the
motion ambiguity, i.e., there exist numerous geometric configurations of
clothes that are dependent on the context of motion even for the same pose. In
this paper, we introduce a method for high-quality modeling of clothed 3D human
avatars using a video of a person with dynamic movements. The main challenge
comes from the lack of 3D ground truth data of geometry and its temporal
correspondences. We address this challenge by introducing a novel compositional
human modeling framework that takes advantage of both explicit and implicit
human modeling. For explicit modeling, a neural network learns to generate
point-wise shape residuals and appearance features of a 3D body model by
comparing its 2D rendering results and the original images. This explicit model
allows for the reconstruction of discriminative 3D motion features from UV
space by encoding their temporal correspondences. For implicit modeling, an
implicit network combines the appearance and 3D motion features to decode
high-fidelity clothed 3D human avatars with motion-dependent geometry and
texture. The experiments show that our method can generate a large variation of
secondary motion in a physically plausible way.

使用一个人的动态视频，通过引入新颖的组合式人体建模框架，结合显式建模和隐式建模的方法，实现高质量的服装 3D 人体化身建模，解决了缺乏几何和其时间对应关系的 3D 真值数据的挑战，成功地生成了具有运动相关几何和纹理的服装 3D 人体化身。

使用单目摄像头实现衣着 3D 人体角色的动态外貌建模

Dynamic Appearance Modeling of Clothed 3D Human Avatars using a Single  Camera

Recent progress in 4D implicit representation focuses on globally controlling
the shape and motion with low dimensional latent vectors, which is prone to
missing surface details and accumulating tracking error. While many deep local
representations have shown promising results for 3D shape modeling, their 4D
counterpart does not exist yet. In this paper, we fill this blank by proposing
a novel Local 4D implicit Representation for Dynamic clothed human, named LoRD,
which has the merits of both 4D human modeling and local representation, and
enables high-fidelity reconstruction with detailed surface deformations, such
as clothing wrinkles. Particularly, our key insight is to encourage the network
to learn the latent codes of local part-level representation, capable of
explaining the local geometry and temporal deformations. To make the inference
at test-time, we first estimate the inner body skeleton motion to track local
parts at each time step, and then optimize the latent codes for each part via
auto-decoding based on different types of observed data. Extensive experiments
demonstrate that the proposed method has strong capability for representing 4D
human, and outperforms state-of-the-art methods on practical applications,
including 4D reconstruction from sparse points, non-rigid depth fusion, both
qualitatively and quantitatively.

本文提出了一种名为 LoRD 的新型局部 4D 隐式表示方法，具有 4D 人体建模和局部表示的优点，能够实现高保真度的重建，并在稀疏点的 4D 重建、非刚性深度融合等方面优于现有方法。

LoRD: 用于高保真动态人体建模的本地 4D 隐式表示

LoRD: Local 4D Implicit Representation for High-Fidelity Dynamic Human  Modeling

Communication is a hallmark of intelligence. In this work, we present MIRROR,
an approach to (i) quickly learn human models from human demonstrations, and
(ii) use the models for subsequent communication planning in assistive
shared-control settings. MIRROR is inspired by social projection theory, which
hypothesizes that humans use self-models to understand others. Likewise, MIRROR
leverages self-models learned using reinforcement learning to bootstrap human
modeling. Experiments with simulated humans show that this approach leads to
rapid learning and more robust models compared to existing behavioral cloning
and state-of-the-art imitation learning methods. We also present a
human-subject study using the CARLA simulator which shows that (i) MIRROR is
able to scale to complex domains with high-dimensional observations and
complicated world physics and (ii) provides effective assistive communication
that enabled participants to drive more safely in adverse weather conditions.

利用强化学习快速学习人类模型，从而在智能辅助控制系统中通过 MIRROR 方法进行有效的人际通信规划。