  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:
https://holistic-motion2d.github.io.
