FlexiClip: Locality-Preserving Free-Form Character Animation

Anant Khandelwal

TL;DR: clipart animation, temporal Jacobians, probability flow ODEs, flow matching loss, GFlowNet, continuous-time modeling, character animation

Abstract: Animating clipart images with seamless motion while maintaining visual fidelity and temporal coherence presents significant challenges. Existing methods, such as AniClipart, effectively model spatial deformations but often fail to ensure smooth temporal transitions, resulting in artifacts like abrupt motions and geometric distortions. Similarly, text-to-video (T2V) and image-to-video (I2V) models struggle to handle clipart due to the mismatch in statistical properties between natural video and clipart styles. This paper introduces FlexiClip, a novel approach designed to overcome these limitations by addressing the intertwined challenges of temporal consistency and geometric integrity. FlexiClip extends traditional Bézier curve-based trajectory modeling with key innovations: temporal Jacobians to correct motion dynamics incrementally, continuous-time modeling via probability flow ODEs (pfODEs) to mitigate temporal noise, and a flow matching loss inspired by GFlowNet principles to optimize smooth motion transitions. These enhancements ensure coherent animations across complex scenarios involving rapid movements and non-rigid deformations. Extensive experiments validate the effectiveness of FlexiClip in generating animations that are not only smooth and natural but also structurally consistent across diverse clipart types, including humans and animals. By integrating spatial and temporal modeling with pre-trained video diffusion models, FlexiClip sets a new standard for high-quality clipart animation, offering robust performance across a wide range of visual content. Project Page: https://creative-gen.github.io/flexiclip.github.io/

Lay Summary:

Animating simple drawings, like clipart images, has traditionally required artists to create each frame by hand. Automated tools often produce jerky movements or distort the original artwork, especially during rapid motions.

**FlexiClip** introduces a new method to animate still clipart images smoothly, preserving the character's original style and structure. By calculating small, continuous movements for each part of the drawing, FlexiClip ensures fluid transitions between frames without sudden jumps or unnatural distortions.

Key innovations include:

* **Continuous Motion Modeling**: Treating animation as a seamless flow rather than separate steps, reducing flickering and stuttering.

* **Learning from Examples**: Mimicking natural movement patterns observed in hand-drawn animations to enhance realism.

* **Detail Preservation**: Maintaining essential features like facial expressions and clothing details throughout the animation.

Tests on various clipart styles demonstrated that FlexiClip produces more natural and faithful animations compared to existing tools. Observers noted smoother movements and better preservation of the characters' original appearances.

By simplifying the animation process, FlexiClip empowers educators, small businesses, and content creators to bring their illustrations to life efficiently. However, users should apply this technology responsibly to avoid creating misleading or deceptive content.

*For more details, refer to the project page: [https://creative-gen.github.io/flexiclip.github.io/](https://creative-gen.github.io/flexiclip.github.io/).*

Application-Driven Machine Learning: This submission is on Application-Driven Machine Learning.

Link To Code: https://creative-gen.github.io/flexiclip.github.io/

Primary Area: Deep Learning->Generative Models and Autoencoders

Keywords: Text-to-Video generation

Submission Number: 909