Fast Sprite Decomposition from Animated Graphics (2408.03923v1)

Abstract: This paper presents an approach to decomposing animated graphics into sprites, a set of basic elements or layers. Our approach builds on the optimization of sprite parameters to fit the raster video. For efficiency, we assume static textures for sprites to reduce the search space while preventing artifacts using a texture prior model. To further speed up the optimization, we introduce the initialization of the sprite parameters utilizing a pre-trained video object segmentation model and user input of single frame annotations. For our study, we construct the Crello Animation dataset from an online design service and define quantitative metrics to measure the quality of the extracted sprites. Experiments show that our method significantly outperforms baselines for similar decomposition tasks in terms of the quality/efficiency tradeoff.

• The paper presents a fast method to decompose animated graphics into sprites using static texture assumptions and minimal user input.
• It employs a texture prior model driven by convolutional networks to optimize sprite parameters and prevent artifacts.
• Validated on the Crello Animation dataset, the approach outperforms baselines in initialization quality and convergence speed.

Fast Sprite Decomposition from Animated Graphics

The paper, "Fast Sprite Decomposition from Animated Graphics," introduces an efficient method to decompose animated graphics into their fundamental elements—sprites. This approach leverages optimization techniques to fit sprite parameters to raster video, assuming static textures for enhanced efficiency. Importantly, the paper constructs the Crello Animation dataset to benchmark and validate the method's effectiveness.

Motivation and Challenge

Animated graphics, extensively used in social media posts and advertisements, are composed of sprites that allow intuitive manipulations. However, post-composition editing of these rasterized videos is almost impossible without decomposing them back into constituent sprites. Unlike natural scene decomposition, animated graphics feature more diverse and numerous objects, including backgrounds, illustrations, and text, each exhibiting different dynamics. Any artifacts in the decomposition process are unacceptable in video editing applications; thus, a balance between the resolution of textures and the efficiency of parameter optimization is paramount.

Methodology

The paper introduces several key innovations to address these challenges:

1. Static Texture Assumption: All textures are presumed static, with only animation parameters changing over time. This significantly reduces the parameter space, enhancing computational efficiency.
2. Texture Prior Model: An image-prior model prevents artifacts by re-formulating texture optimization, representing textures as outputs of a convolutional neural network driven by texture codes.
3. Efficient Initialization: Utilizing a pre-trained video object segmentation model and minimal user input for single-frame annotations, the method quickly initializes sprite parameters, fostering faster convergence during optimization.

Crello Animation Dataset

The research establishes the Crello Animation dataset sourced from an online design service. This dataset, distinct from natural video datasets, includes various templates with intricate animated designs specifically for social media platforms. Each template in the dataset is thoroughly annotated, offering a robust basis for quantitative evaluation of sprite decomposition methods.

Experimental Results

Experiments demonstrate the proposed method's superiority in the quality/efficiency trade-off by comparing its performance against established baselines such as Layered Neural Atlases (LNA) and Deformable Sprites (DS). The key findings include:

• Improved Initialization: The method significantly outperforms existing approaches when initial parameter settings are vital. For example, in 10 minutes of optimization, the method achieves markedly lower frame and sprite errors.
• Faster Convergence: Leveraging the static texture assumption and initialization techniques, the approach yields faster convergence while maintaining high decomposition quality.

These quantitative results are supported by qualitative analyses where the method effectively handles complex sprites without substantial artifacts, even in scenarios involving intricate overlapping and varied animations.

Implications and Future Research

Practically, this optimized sprite decomposition process facilitates enhanced video editing workflows, enabling users to manipulate detailed animated graphics efficiently. Theoretically, the findings underscore the importance of effective initialization and domain-specific assumptions in video analysis tasks.

Future research might explore relaxing the static texture assumption by parameterizing more complex animation dynamics. Additionally, incorporating more types of animation effects, such as lighting changes and blur effects, could expand the method's applicability in creative workflows. Also, integrating the decomposition method with existing video editing software could offer real-time video editing capabilities, further bridging the gap between rasterized outputs and editable sprite-based animations.

Overall, this research lays significant groundwork for optimizing animated graphic manipulations and sets a promising direction for future enhancements in video editing technologies.