The paper proposes a novel two-stage training scheme for pose-guided text-to-video generation using easily obtainable datasets, and pre-trained text-to-image (T2I) models, which enables the generation of pose-controllable character videos while maintaining the editing and concept composition abilities of T2I models.

Key insights and lessons learned:

• The proposed two-stage training scheme can effectively generate pose-controllable character videos.
• The use of easily obtainable datasets and pre-trained T2I models makes the method more practical and feasible for real-world applications.
• The addition of learnable temporal self-attention and reformed cross-frame self-attention blocks improves the motion of the generated videos.
• The method can maintain the editing and concept composition abilities of T2I models, allowing for more flexibility and creativity in video generation.

Questions for the authors:

1. How does the proposed method compare to other state-of-the-art methods for pose-guided text-to-video generation?
2. Have you considered the ethical implications of this technology, such as the potential for misuse in creating deepfakes or other forms of deceptive content?
3. How generalizable is the proposed method to different types of characters or poses?
4. Can the method be extended to incorporate audio or other modalities to further improve the realism of the generated videos?
5. How would you envision this technology being used in practical applications, such as in the entertainment industry or for virtual assistants?

Suggestions for future research:

1. Investigate the use of additional modalities, such as audio or haptic feedback, to enhance the realism and interactivity of generated videos.
2. Explore the potential for using generative models to generate novel poses or movements beyond those in the training data.
3. Evaluate the proposed method on a larger and more diverse dataset to assess its generalizability and robustness.
4. Investigate the potential for using the method in other domains beyond character animation, such as in sports analysis or medical imaging.
5. Develop techniques for detecting and mitigating the potential for misuse of this technology, such as in detecting deepfakes or preventing unauthorized use of personal images or videos.

References:

1. Park, T., Liu, M. Y., Wang, T. C., & Zhu, J. Y. (2019). Semantic image synthesis with spatially-adaptive normalization. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 2337-2346).