Human image animation involves generating videos from a character photo, allowing user control and unlocking the 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 real-world videos from the internet. We developed and applied careful filtering rules to ensure video quality, resulting in a curated collection of 20K high-resolution (1080P) human-centric videos. Human and camera motion annotation is accomplished using a 2D pose estimator and a SLAM-based method. To expand our synthetic dataset, we collected 10K 3D avatar assets and leveraged existing assets of body shapes, skin textures and clothings. 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. Demo, data and code could be found in the project website: https://humanvid.github.io/.

Two-stream based architectures for video action recognition exhibit great success recently. They encode the appearance with RGB frame, and the motion with optical flow. It is observed that optical flow depicts pixel-level motion field, focusing much on detail information, is hard to tackle the large displacement. In fact, human always focus the global motion rather than pixel-level motion. Inspired by this, we propose a novel 3-stream network structure with a spatial ConvNet, a pixel-level temporal ConvNet and a block-level temporal ConvNet. Integrating multi-granularity motion representation significantly outperforms single pixel-level motion field based architectures. Further, we can obtain the block-level motion vector field from compressed videos without extra calculation. We address missing and noisy motion patterns of motion vector field with intra-encoded block rectifying and flow guided filtering, building a hybrid motion field for our block-level temporal ConvNet. Our approach obtains state-of-the-art accuracy on UCF101 (95.27%) and HMDB 51 (69.21 %).