In this paper, we introduce a novel path to $\textit{general}$ human motion
generation by focusing on 2D space. Traditional methods have primarily
generated human motions in 3D, which, while detailed and realistic, are often
limited by the scope of available 3D motion data in terms of both the size and
the diversity. To address these limitations, we exploit extensive availability
of 2D motion data. We present $\textbf{Holistic-Motion2D}$, the first
comprehensive and large-scale benchmark for 2D whole-body motion generation,
which includes over 1M in-the-wild motion sequences, each paired with
high-quality whole-body/partial pose annotations and textual descriptions.
Notably, Holistic-Motion2D is ten times larger than the previously largest 3D
motion dataset. We also introduce a baseline method, featuring innovative
$\textit{whole-body part-aware attention}$ and $\textit{confidence-aware
modeling}$ techniques, tailored for 2D $\underline{\text
T}$ext-driv$\underline{\text{EN}}$ whole-bo$\underline{\text D}$y motion
gen$\underline{\text{ER}}$ation, namely $\textbf{Tender}$. Extensive
experiments demonstrate the effectiveness of $\textbf{Holistic-Motion2D}$ and
$\textbf{Tender}$ in generating expressive, diverse, and realistic human
motions. We also highlight the utility of 2D motion for various downstream
applications and its potential for lifting to 3D motion. The page link is:
this https URL

通过关注二维空间，本研究引入一种新的一般人类动作生成方法，使用名为 Holistic-Motion2D 的大规模二维整体动作数据库和名为 Tender 的创新方法进行生成，展示了二维动作数据在生成多样、真实的人类动作方面的有效性及其对各种下游应用的潜力。

全身动作生成 2D 空间中的可扩展性

Holistic-Motion2D: Scalable Whole-body Human Motion Generation in 2D  Space

Key information extraction (KIE) from document images requires understanding
the contextual and spatial semantics of texts in two-dimensional (2D) space.
Many recent studies try to solve the task by developing pre-trained language
models focusing on combining visual features from document images with texts
and their layout. On the other hand, this paper tackles the problem by going
back to the basic: effective combination of text and layout. Specifically, we
propose a pre-trained language model, named BROS (BERT Relying On Spatiality),
that encodes relative positions of texts in 2D space and learns from unlabeled
documents with area-masking strategy. With this optimized training scheme for
understanding texts in 2D space, BROS shows comparable or better performance
compared to previous methods on four KIE benchmarks (FUNSD, SROIE*, CORD, and
SciTSR) without relying on visual features. This paper also reveals two
real-world challenges in KIE tasks-(1) minimizing the error from incorrect text
ordering and (2) efficient learning from fewer downstream examples-and
demonstrates the superiority of BROS over previous methods. Code is available
at this https URL

提出一种名为 BROS（BERT Relying On Spatiality）的预训练语言模型，重点在于将文字和布局有效组合起来，通过对文档中文字的相对位置进行编码以学习二维空间中的文本语义和布局，并利用面积屏蔽策略从未标记文档进行优化训练。BROS 表现出与其他方法相当甚至更好的性能，解决了两个实际的关键信息提取问题：减少文本顺序错误和使用较少的下游示例进行高效学习。