HumanVid: Demystifying Training Data for Camera-controllable Human Image Animation
(2407.17438)Abstract
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/.
Overview
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The paper introduces HumanVid, a large-scale high-quality dataset for human image animation that integrates real-world and synthetic data to offer diverse human video content.
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CamAnimate, a baseline model introduced by the authors, efficiently decouples and learns human and camera motions, demonstrating superior performance against state-of-the-art methods.
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Extensive experiments validate CamAnimate's performance through various metrics, highlighting its robustness and the efficacy of the HumanVid dataset in enhancing human image animation technologies.
Analysis of "HumanVid: Demystifying Training Data for Camera-controllable Human Image Animation"
The paper "HumanVid: Demystifying Training Data for Camera-controllable Human Image Animation" by Zhenzhi Wang et al. presents a significant advancement in the field of human image animation. The authors introduce HumanVid, which comprises a large-scale high-quality dataset tailored for human image animation, integrating both crafted real-world and synthetic data. They also propose a baseline model named CamAnimate, which considers both human and camera motions as conditions, demonstrating superior performance in the domain.
Key Contributions
The primary contributions of the paper are manifold:
Introduction of HumanVid Dataset:
- The dataset includes 20K human-centric videos collected from the internet, all in 1080P resolution, ensuring high quality.
- The synthetic portion of the dataset is built from 2,300 copyright-free 3D avatar assets, with precise annotations of camera and human motions, thus offering diverse and high-fidelity human videos.
Synthetic Data Construction:
- The authors leverage Unreal Engine 5 (UE5) and Blender to render videos, characterized by realistic human appearance and diverse camera trajectories.
- The dataset includes various human-like and anime characters, detailed differently in body shapes, skin tones, and clothing textures, enhancing the diversity and utility of the dataset.
Innovations in Camera Trajectory Design:
- A novel rule-based camera trajectory generation method ensures diverse and precise camera motion annotations, compensating for the limitations observed in real-world data.
CamAnimate Baseline Model:
- By integrating state-of-the-art techniques for camera control and human animation, CamAnimate efficiently decouples and learns these motions in an end-to-end manner.
- The provided results showcase CamAnimate's ability to achieve superior performance in the task of human image animation with explicit camera movements.
Evaluation and Results
The performance of CamAnimate is validated through extensive experiments, benchmarking against state-of-the-art methods such as Animate Anyone, Magic-animate, and Champ. The evaluation protocols employed include metrics like PSNR, SSIM, LPIPS, FID, and FVD, under both static and moving camera conditions. Crucially, CamAnimate outperformed these methods across all metrics, evidencing the efficacy of the novel dataset and the robustness of the proposed model.
Implications and Future Directions
This work has substantial implications for the domain of human image animation and video generation. With the accurate motion annotations and diverse data provided by HumanVid, researchers can now train models that handle complex human and camera movements more effectively. This can lead to advancements in various applications such as virtual reality, augmented reality, and movie production.
From a theoretical standpoint, the integration of synthetic data with real-world data presents a compelling case for the role of such mixed datasets in achieving high-quality results in generative tasks. Future developments could explore more sophisticated models building upon the CamAnimate baseline, possibly integrating more nuanced understanding of human movements and interactions within varying environments.
One key aspect for future research could be the enhancement of annotation accuracy, especially in real-world data. The reliance on pose estimation and SLAM methods, while effective, could introduce noise. Thus, refining these annotation techniques or incorporating additional supervision could further bolster the quality and utility of training datasets.
Conclusion
The HumanVid dataset and the CamAnimate model mark a notable advancement in the field of human image animation, laying down a robust foundation for future research. By addressing the dual challenges of dataset accessibility and the consideration of camera movements, the authors significantly contribute to fair and transparent benchmarking in this domain. Researchers and practitioners can leverage these contributions to push the boundaries of video and movie production technologies, though they must remain cognizant of the broader impacts and ethical considerations of such potent tools.
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GitHub - zhenzhiwang/HumanVid (206 stars)