The recent advancements in video generation and inpainting have shown a significant shift towards unified and scalable frameworks that integrate multiple tasks within a single model. Researchers are increasingly focusing on developing models that can handle a variety of video-related tasks, such as inpainting, interpolation, and generation, simultaneously. This approach not only enhances the performance of individual tasks but also allows for mutual enhancement between them, leading to more robust and versatile solutions.

One notable trend is the adoption of Mixture-of-Experts (MoE) attention mechanisms, which enable models to handle diverse tasks more effectively by leveraging specialized sub-networks. This has been particularly effective in video inpainting and interpolation, where the integration of spatial-temporal masking strategies has shown to improve performance.

Another significant development is the integration of text and image conditioning in video generation models, which has led to the creation of scalable and flexible frameworks capable of handling both text-to-video and text-image-to-video tasks. These models, designed with simplicity and extensibility in mind, have demonstrated state-of-the-art performance across various benchmarks, paving the way for more versatile video generation solutions.

Character consistency in text-to-video generation has also seen innovative solutions, with methods that balance identity preservation and natural motion retention, improving the quality and coherence of generated videos. Additionally, the repurposing of pre-trained video diffusion models for event-based video interpolation has shown promising results, addressing the limitations of traditional methods with limited training data.

In the realm of animated sticker generation, the introduction of large-scale vision-language datasets and specialized layers for semantic interaction has opened new avenues for research, contributing to the development of more sophisticated and user-friendly creation tools.

Noteworthy papers include:

• UniPaint, which introduces a unified framework for video inpainting and interpolation using MoE attention.
• STIV, a scalable text-image-conditioned video generation method that achieves state-of-the-art results.
• Video Storyboarding, which improves character consistency in text-to-video generation through a novel query injection strategy.