AniClipart: Generating Cartoon-Style Animations from Clipart using Text-to-Video Priors

Khái niệm cốt lõi

AniClipart leverages text-to-video diffusion models and as-rigid-as-possible shape deformation to transform static clipart into high-quality, cartoon-style animations that align with provided text prompts while preserving the visual identity of the original clipart.

Tóm tắt

The paper introduces AniClipart, a system that can automatically animate static clipart images based on text descriptions. The key aspects of the method are:

Clipart Preprocessing:

Detect keypoints and build skeletons on the clipart using a hybrid approach, combining off-the-shelf keypoint detection algorithms and custom skeletonization for broader object categories.
Construct a triangular mesh over the clipart for shape deformation.

Bézier-Driven Animation:

Assign a cubic Bézier curve as the motion trajectory for each keypoint, ensuring smooth transitions between frames.
Optimize the Bézier curve parameters using Video Score Distillation Sampling (VSDS) loss, which distills motion knowledge from a pretrained text-to-video diffusion model to align the animation with the provided text prompt.
Incorporate a skeleton loss to maintain the rigidity and visual identity of the original clipart during deformation.
Use a differentiable As-Rigid-As-Possible (ARAP) shape deformation algorithm to warp the clipart according to the updated keypoint positions.

Layered Animation:

Extend the system to handle layered clipart, allowing for animations with topological changes and self-occlusion.

Extensive experiments and ablation studies demonstrate that AniClipart outperforms existing image-to-video generation models in terms of text-video alignment, visual identity preservation, and motion consistency. The system also showcases versatility by adapting to generate a broader array of animation formats.

Thống kê

"A galloping dog."
"A dolphin bends its body flexibly."
"A young girl is jumping."
"A man is scuba diving and swaying fins."
"A woman is dancing."
"A woman bends arms."
"A woman is stomping."

Trích dẫn

"A galloping dog."
"A dolphin bends its body flexibly."
"A young girl is jumping."
"A man is scuba diving and swaying fins."
"A woman is dancing."
"A woman bends arms."
"A woman is stomping."

Thông tin chi tiết chính được chắt lọc từ

AniClipart: Clipart Animation with Text-to-Video Priors

by Ronghuan Wu,... lúc arxiv.org 04-19-2024

https://arxiv.org/pdf/2404.12347.pdf

Yêu cầu sâu hơn

How could AniClipart be extended to handle more complex motion patterns, such as interactions between multiple characters or physics-based simulations?

AniClipart could be extended to handle more complex motion patterns by incorporating advanced techniques and algorithms. One approach could be to implement a hierarchical keypoint detection system that can identify key points not only on individual characters but also on multiple characters within a scene. By detecting key points on each character and defining interactions between these key points, the system can generate animations depicting interactions between multiple characters.
Furthermore, integrating physics-based simulations into the animation process can add a layer of realism to the movements. By incorporating physics engines or algorithms that simulate real-world physics principles like gravity, friction, and collision detection, AniClipart can create animations with more natural and dynamic movements. This would enable the system to generate animations where characters interact with their environment and with each other in a more realistic manner.

What are the potential limitations of using text-to-video diffusion models as the motion prior, and how could these be addressed in future work?

Using text-to-video diffusion models as the motion prior in AniClipart may have some limitations that could impact the quality and realism of the generated animations. One limitation is the potential mismatch between the motion patterns learned by the diffusion model and the desired cartoon-style motions of clipart animations. Diffusion models are trained on a diverse range of natural videos, which may not capture the simplistic and exaggerated movements typical of cartoon animations.
To address this limitation, future work could involve fine-tuning the text-to-video diffusion models on a dataset specifically curated for cartoon-style animations. By training the models on a dataset that includes a wide variety of cartoon animations, the models can learn motion priors that are more aligned with the desired style of clipart animations. Additionally, incorporating style transfer techniques or domain adaptation methods could help bridge the gap between the motion patterns learned by the diffusion models and the motion characteristics of clipart animations.
Another limitation could be the scalability and computational complexity of using large-scale diffusion models for generating animations in real-time or for a large number of clipart images. Future work could focus on optimizing the inference process and exploring more efficient architectures that can generate animations quickly without compromising quality.

Could the AniClipart framework be adapted to generate animations for other types of visual content beyond 2D clipart, such as 3D models or hand-drawn sketches?

Yes, the AniClipart framework could be adapted to generate animations for other types of visual content beyond 2D clipart, such as 3D models or hand-drawn sketches. To extend the framework to support 3D models, the keypoint detection and deformation algorithms would need to be modified to operate in a 3D space. By detecting key points on 3D models and defining motion trajectories in three dimensions, the system can generate animations for 3D objects with similar principles used for clipart animations.
For hand-drawn sketches, the framework could be adapted to incorporate sketch recognition algorithms that can identify key features and movements in the sketches. By defining key points on hand-drawn sketches and utilizing similar motion regularization techniques, AniClipart can generate animations that bring hand-drawn sketches to life.
Overall, with the appropriate modifications and enhancements, the AniClipart framework can be versatile enough to generate animations for various types of visual content, expanding its applicability to a broader range of animation styles and formats.