AniDoc: Animation Creation Made Easier (2412.14173v2)

Abstract: The production of 2D animation follows an industry-standard workflow, encompassing four essential stages: character design, keyframe animation, in-betweening, and coloring. Our research focuses on reducing the labor costs in the above process by harnessing the potential of increasingly powerful generative AI. Using video diffusion models as the foundation, AniDoc emerges as a video line art colorization tool, which automatically converts sketch sequences into colored animations following the reference character specification. Our model exploits correspondence matching as an explicit guidance, yielding strong robustness to the variations (e.g., posture) between the reference character and each line art frame. In addition, our model could even automate the in-betweening process, such that users can easily create a temporally consistent animation by simply providing a character image as well as the start and end sketches. Our code is available at: https://yihao-meng.github.io/AniDoc_demo.

• The paper introduces a video diffusion model that automates 2D animation colorization and in-betweening to reduce manual labor and production time.
• It employs a novel correspondence matching mechanism to ensure consistent color fidelity across varying sketches and poses.
• Empirical results demonstrate marked improvements in PSNR, SSIM, LPIPS, and FVD, highlighting its superiority over previous methods.

Overview of AniDoc: Animation Creation Made Easier

The paper entitled "AniDoc: Animation Creation Made Easier" approaches transformational advancements in the automated colorization of 2D animations. The work primarily focuses on leveraging video diffusion models to reduce the labor-intensive and time-consuming nature of animation production, specifically in the domain of sketch colorization and in-betweening. This research builds upon existing generative AI models to enhance the efficiency and accuracy of line art colorization, with implications for significantly optimizing the animation workflow.

The paper outlines a methodology hinged on video diffusion models specifically tailored for animation colorization tasks. A critical aspect of this model is the introduction of a correspondence matching mechanism to address variations between reference character designs and individual line art frames. This component is instrumental in achieving high fidelity in colorization by robustly maintaining consistency in colors and styles across disparate poses and scales in sketches.

The model demonstrates an impressive capability to automate the in-betweening process. It uses sparse sketch inputs to interpolate frames, thereby facilitating smooth and consistent animations from minimal input data. This feature allows creators to generate animations by providing just the character's design and the starting and ending frames.

Key empirical evaluations reveal the model's efficacy in achieving superior quantitative and qualitative performances compared to pre-existing methods. The experiments highlight significant improvements in metrics such as PSNR, SSIM, LPIPS for accuracy, and FVD for video coherence. Such results underscore the methodological strength of integrating explicit correspondence into a video generation framework.

Implications and Prospective Directions

Practical Implications:

1. Labor and Cost Efficiency: By automating traditionally manual processes in animation production, the method promises substantial reductions in labor costs and production timelines. This is pertinent for industries like anime, which are typically labor-intensive and time-strained.
2. High-Fidelity Preservation: The proposed model maintains high fidelity to character designs, effectively minimizing color inaccuracies that can detract from viewer experience. This is achieved through sophisticated correspondence matching which ensures detailed color alignment with reference designs.

Theoretical Contributions:

1. Novel Use of Video Diffusion Models: This paper demonstrates the versatility of diffusion models beyond their conventional applications. By extending these models to domain-specific challenges such as animation, the research expands the theoretical framework under which diffusion models can be utilized.
2. Interdisciplinary Application: By bridging AI with traditional art forms, this paper contributes to an interplay that could inspire further interdisciplinary research, fostering innovation in both AI and creative industries.

Speculation on AI Developments:

Into the future, the implications of this research might fuel developments in AI-driven creative processes. Animation, as a complex artistic field, still stands to benefit from future advancements in model accuracy, efficiency, and perhaps interactivity, allowing animators to iterate designs with minimal constraints. Enhancements in hardware acceleration and model compression could facilitate these models' deployment on edge devices, making powerful animation tools accessible globally.

Further research could explore the integration of real-time feedback mechanisms for artists, allowing interactive adjustments during the creation process. Additionally, expanding the model to handle more diverse animation styles and formats could broaden its applicability and appeal in various artistic domains.

In summary, the paper sets a solid foundation for innovation in automated animation workflows, suggesting a promising trajectory for AI applications in digital creative fields. As AI models continue to improve, we can expect increasing sophistication in both the technical and creative outputs achievable in animation production.