  Human image animation involves generating videos from a character photo,
allowing user control and unlocking potential for video and movie production.
While recent approaches yield impressive results using high-quality training
data, the inaccessibility of these datasets hampers fair and transparent
benchmarking. Moreover, these approaches prioritize 2D human motion and
overlook the significance of camera motions in videos, leading to limited
control and unstable video generation. To demystify the training data, we
present HumanVid, the first large-scale high-quality dataset tailored for human
image animation, which combines crafted real-world and synthetic data. For the
real-world data, we compile a vast collection of copyright-free real-world
videos from the internet. Through a carefully designed rule-based filtering
strategy, we ensure the inclusion of high-quality videos, resulting in a
collection of 20K human-centric videos in 1080P resolution. Human and camera
motion annotation is accomplished using a 2D pose estimator and a SLAM-based
method. For the synthetic data, we gather 2,300 copyright-free 3D avatar assets
to augment existing available 3D assets. Notably, we introduce a rule-based
camera trajectory generation method, enabling the synthetic pipeline to
incorporate diverse and precise camera motion annotation, which can rarely be
found in real-world data. To verify the effectiveness of HumanVid, we establish
a baseline model named CamAnimate, short for Camera-controllable Human
Animation, that considers both human and camera motions as conditions. Through
extensive experimentation, we demonstrate that such simple baseline training on
our HumanVid achieves state-of-the-art performance in controlling both human
pose and camera motions, setting a new benchmark. Code and data will be
publicly available at https://github.com/zhenzhiwang/HumanVid/.
