We propose SplatArmor, a novel approach for recovering detailed and
animatable human models by `armoring' a parameterized body model with 3D
Gaussians. Our approach represents the human as a set of 3D Gaussians within a
canonical space, whose articulation is defined by extending the skinning of the
underlying SMPL geometry to arbitrary locations in the canonical space. To
account for pose-dependent effects, we introduce a SE(3) field, which allows us
to capture both the location and anisotropy of the Gaussians. Furthermore, we
propose the use of a neural color field to provide color regularization and 3D
supervision for the precise positioning of these Gaussians. We show that
Gaussian splatting provides an interesting alternative to neural rendering
based methods by leverging a rasterization primitive without facing any of the
non-differentiability and optimization challenges typically faced in such
approaches. The rasterization paradigms allows us to leverage forward skinning,
and does not suffer from the ambiguities associated with inverse skinning and
warping. We show compelling results on the ZJU MoCap and People Snapshot
datasets, which underscore the effectiveness of our method for controllable
human synthesis.

我们提出了一种名为 SplatArmor 的新方法，通过在 3D 高斯函数中将参数化的人体模型 “装甲”，从而恢复出详细的、可动画的人体模型。我们的方法将人体表示为一组位于规范空间内的 3D 高斯函数，其关节扭曲通过在规范空间内任意位置扩展底层 SMPL 几何体的蒙皮来定义。为了考虑姿势相关效果，我们引入了一个 SE (3) 场，允许捕捉高斯函数的位置和各向异性。此外，我们提出了使用神经颜色场来提供颜色规范化和用于准确定位这些高斯函数的 3D 监督。我们通过展示 ZJU MoCap 和 People Snapshot 数据集上的令人信服的结果，证明了高斯函数散射提供了一种有趣的替代于基于神经渲染的方法，它利用了不可区分性和优化挑战，而这些挑战通常在这类方法中面临。粗放复刻范例使我们能够利用向前蒙皮，并且不会受到逆蒙皮和扭曲所带来的不确定性的影响。我们的方法对于可控的人体合成的有效性进行了强有力的证明。

SplatArmor：来自单目 RGB 视频的可动人体的关节高斯样贴片渲染

SplatArmor: Articulated Gaussian splatting for animatable humans from  monocular RGB videos

Human models play a crucial role in human-robot interaction (HRI), enabling
robots to consider the impact of their actions on people and plan their
behavior accordingly. However, crafting good human models is challenging;
capturing context-dependent human behavior requires significant prior knowledge
and/or large amounts of interaction data, both of which are difficult to
obtain. In this work, we explore the potential of large-language models (LLMs)
-- which have consumed vast amounts of human-generated text data -- to act as
zero-shot human models for HRI. Our experiments on three social datasets yield
promising results; the LLMs are able to achieve performance comparable to
purpose-built models. That said, we also discuss current limitations, such as
sensitivity to prompts and spatial/numerical reasoning mishaps. Based on our
findings, we demonstrate how LLM-based human models can be integrated into a
social robot's planning process and applied in HRI scenarios. Specifically, we
present one case study on a simulated trust-based table-clearing task and
replicate past results that relied on custom models. Next, we conduct a new
robot utensil-passing experiment (n = 65) where preliminary results show that
planning with a LLM-based human model can achieve gains over a basic myopic
plan. In summary, our results show that LLMs offer a promising (but incomplete)
approach to human modeling for HRI.

本文探索了使用大型语言模型作为 HRI 的 0 模型的潜力，并在三个社交数据集上进行了实验，结果显示 LLMs 能够实现与定制模型相当的性能，同时还讨论了当前的限制。基于我们的发现，我们展示了 LLM 人类模型如何集成到社交机器人的规划过程中并应用于 HRI 场景。我们的结果表明 LLMs 为 HRI 的人类建模提供了一种有前途但不完整的方法。

作为零样本人类模型的大型语言模型用于人机交互

Large Language Models as Zero-Shot Human Models for Human-Robot Interaction

To make 3D human avatars widely available, we must be able to generate a
variety of 3D virtual humans with varied identities and shapes in arbitrary
poses. This task is challenging due to the diversity of clothed body shapes,
their complex articulations, and the resulting rich, yet stochastic geometric
detail in clothing. Hence, current methods to represent 3D people do not
provide a full generative model of people in clothing. In this paper, we
propose a novel method that learns to generate detailed 3D shapes of people in
a variety of garments with corresponding skinning weights. Specifically, we
devise a multi-subject forward skinning module that is learned from only a few
posed, un-rigged scans per subject. To capture the stochastic nature of
high-frequency details in garments, we leverage an adversarial loss formulation
that encourages the model to capture the underlying statistics. We provide
empirical evidence that this leads to realistic generation of local details
such as wrinkles. We show that our model is able to generate natural human
avatars wearing diverse and detailed clothing. Furthermore, we show that our
method can be used on the task of fitting human models to raw scans,
outperforming the previous state-of-the-art.

这篇论文提出了一种新方法，使用前向蒙皮模块和对抗网络模型，仅从少量生动的数据中学习生成多样化的 3D 人形模型，以便能够更自然地生成身着不同服装的人形模型，并且还可用于将人形模型拟合到原始扫描数据上。