AniClipart: Clipart Animation with Text-to-Video Priors
(2404.12347)Abstract
Clipart, a pre-made graphic art form, offers a convenient and efficient way of illustrating visual content. Traditional workflows to convert static clipart images into motion sequences are laborious and time-consuming, involving numerous intricate steps like rigging, key animation and in-betweening. Recent advancements in text-to-video generation hold great potential in resolving this problem. Nevertheless, direct application of text-to-video generation models often struggles to retain the visual identity of clipart images or generate cartoon-style motions, resulting in unsatisfactory animation outcomes. In this paper, we introduce AniClipart, a system that transforms static clipart images into high-quality motion sequences guided by text-to-video priors. To generate cartoon-style and smooth motion, we first define B\'{e}zier curves over keypoints of the clipart image as a form of motion regularization. We then align the motion trajectories of the keypoints with the provided text prompt by optimizing the Video Score Distillation Sampling (VSDS) loss, which encodes adequate knowledge of natural motion within a pretrained text-to-video diffusion model. With a differentiable As-Rigid-As-Possible shape deformation algorithm, our method can be end-to-end optimized while maintaining deformation rigidity. Experimental results show that the proposed AniClipart consistently outperforms existing image-to-video generation models, in terms of text-video alignment, visual identity preservation, and motion consistency. Furthermore, we showcase the versatility of AniClipart by adapting it to generate a broader array of animation formats, such as layered animation, which allows topological changes.
Overview
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AniClipart introduces a novel approach to animating clipart by using text-to-video priors, preserving the original style and aesthetics of the clipart.
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The system utilizes Bézier curves for defining motion trajectories and optimizes animations using Video Score Distillation Sampling (VSDS) loss.
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Extensive tests show that AniClipart outperforms traditional image-to-video models in maintaining visual identity and aligning animations with textual prompts.
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The technology reduces manual animation efforts and shows potential for broader applications in educational and entertainment media, with future possibilities in 3D animation enhancements.
AniClipart: Enhancing Clipart Animation with Text-to-Video Priors
Introduction
AniClipart introduces a novel approach to animating static clipart images using text-to-video (T2V) priors to dictate motion trajectories. This research leverages advancements in text-to-video diffusion models, aiming to simplify the animation process while preserving the artistic identity of the clipart. The system outlines a method for defining motion using Bézier curves tied to key points on the clipart, optimized through a Video Score Distillation Sampling (VSDS) loss. This enables the generation of animations that are not only smooth and visually coherent but also respectful of the clipart's original style.
Methodology
AniClipart employs several innovative steps to achieve its objectives:
- Keypoint and Skeleton Detection: Utilizes advanced detection algorithms to identify crucial points and establish a skeletal framework on the clipart, which guides subsequent animations.
- Bézier-driven Animation: Motion trajectories for each keypoint are represented as Bézier curves, enabling controlled and smooth animations.
- Loss Functions: Incorporates VSDS loss to ensure movements are in line with specified text prompts. A skeleton preservation loss is also used to maintain structural integrity throughout the animation.
Key innovations include the use of ARAP (As-Rigid-As-Possible) shape manipulation to maintain the rigidity and identity of the clipart during animation. The system's end-to-end optimization capability allows for the efficient tweaking of animation dynamics according to textual descriptions.
Experimental Setup and Results
Extensive experiments demonstrate that AniClipart outperforms existing image-to-video models in various aspects:
- Text-Video Alignment: Ensures that the generated animations are aligned with the text prompts, reflecting the intended motions accurately.
- Visual Identity Preservation: Successfully retains the original aesthetic and structural details of the clipart, a notable improvement over traditional methods that may distort during the animation process.
The system was tested across multiple clipart categories, including humans, animals, and objects, showing its versatility and robustness. Comparison with conventional methods highlights AniClipart's enhanced capability to preserve visual identity and produce semantically meaningful animations.
Implications and Future Work
The development of AniClipart has both practical and theoretical implications for the field of automatic animation:
- Reduction in Manual Effort: By automating key aspects of the animation process, AniClipart significantly reduces the time and effort traditionally required to animate cliparts.
- Broadened Applicability: The method's success with diverse clipart suggests potential applications in other forms of graphic animations, such as educational tools, presentations, and entertainment media.
Looking ahead, potential enhancements could include adapting the system for 3D animation, improving the model's ability to handle complex motion patterns, and refining the text-to-motion alignment to capture nuanced textual descriptions more effectively.
Conclusions
AniClipart represents a significant step forward in the automation of clipart animation, driven by cutting-edge AI techniques. By bridging text-to-video models with clipart animation, this research not only simplifies the animation process but also enhances the creative possibilities, making high-quality animation more accessible. Future developments in this area are poised to further revolutionize how graphical content is animated and used across various digital platforms.
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