Follow-Your-Canvas: Higher-Resolution Video Outpainting with Extensive Content Generation (2409.01055v1)

Abstract: This paper explores higher-resolution video outpainting with extensive content generation. We point out common issues faced by existing methods when attempting to largely outpaint videos: the generation of low-quality content and limitations imposed by GPU memory. To address these challenges, we propose a diffusion-based method called \textit{Follow-Your-Canvas}. It builds upon two core designs. First, instead of employing the common practice of "single-shot" outpainting, we distribute the task across spatial windows and seamlessly merge them. It allows us to outpaint videos of any size and resolution without being constrained by GPU memory. Second, the source video and its relative positional relation are injected into the generation process of each window. It makes the generated spatial layout within each window harmonize with the source video. Coupling with these two designs enables us to generate higher-resolution outpainting videos with rich content while keeping spatial and temporal consistency. Follow-Your-Canvas excels in large-scale video outpainting, e.g., from 512X512 to 1152X2048 (9X), while producing high-quality and aesthetically pleasing results. It achieves the best quantitative results across various resolution and scale setups. The code is released on https://github.com/mayuelala/FollowYourCanvas

• The paper introduces a spatial window strategy that segments video outpainting to overcome GPU memory limitations.
• It integrates the source video’s structure with a Layout Encoder and Relative Region Embedding to ensure spatial-temporal consistency.
• Parallel GPU processing significantly reduces FVD scores, accelerating high-resolution video generation and improving output quality.

Higher-Resolution Video Outpainting with Extensive Content Generation

The paper introduces an innovative approach to video outpainting named "Follow-Your-Canvas," addressing critical limitations in current methodologies associated with GPU memory constraints. Developed by researchers from Tencent and several prominent universities, this method centers on generating higher-resolution video content with improved spatial and temporal consistency through a diffusion-based framework.

Key Contributions

1. Spatial Window Strategy: Unlike traditional "single-shot" outpainting approaches, which input the entire video into the model at once, "Follow-Your-Canvas" employs a spatial window strategy. This tactic divides the video into manageable windows, enabling the generation of extensive content without the heavy burden on GPU memory. By processing smaller video segments sequentially, it mitigates the computational load and allows for outpainting at high resolutions with a greater content expansion ratio.
2. Incorporation of Source Video and Positional Relation: To preserve alignment with the source material, the approach integrates the video’s original structure and spatial relation into the generation process of each window. This includes introducing a Layout Encoder (LE) and Relative Region Embedding (RRE) to ensure that generated layouts within windows harmonize with the overall video structure, improving spatial-temporal consistency.
3. Parallel Processing and Efficiency: The distributed window approach not only enhances the quality and resolution but also allows for parallel processing across multiple GPUs. This capability significantly accelerates generation speed without compromising on quality.

Comparative Analysis

The paper thoroughly evaluates the performance of "Follow-Your-Canvas" against contemporary methods, such as M3DDM and MOTIA. The new method outperforms existing techniques, delivering more aesthetically pleasing and higher fidelity video outputs across various scales and resolutions. Quantitatively, it achieves reductions in FVD scores, reflecting improved coherence and quality in generated videos. For instance, when outpainting from a resolution of 512×512 to 1152×2048, the proposed method significantly reduces FVD from 928.6 to 735.3, indicating strong improvements.

Implications and Future Directions

Practically, the ability to efficiently expand videos to higher resolutions could redefine viewing experiences across diverse platforms, from smartphones to large display applications. It offers versatility in content creation, enabling better adaptation of aspect ratios without losing context or quality.

Theoretically, the paper enhances understanding of diffusion models in generative tasks, suggesting that breaking down computational challenges into distributed tasks can overcome hardware limitations. This insight could propel further research into multi-GPU strategies not just in video generation but broader AI applications involving large datasets or demanding processing requirements.

Future explorations could focus on optimizing the processing speed by enhancing model architecture or developing more sophisticated merging strategies post-denoising. Additionally, integrating adaptive window resizing based on content could refine results, tailoring the generation process more closely to the video’s semantic needs.

In conclusion, "Follow-Your-Canvas" innovatively tackles the persistent challenge of video outpainting at high resolutions, leveraging a clever partitioning strategy enriched by context-aware generation modules. The potential applications and inherent methodological enhancements position it as an exciting development in the field of AI-driven video processing.