LogoMotion: Visually-Grounded Code Synthesis for Creating and Editing Animation (2405.07065v2)

Abstract: Creating animation takes time, effort, and technical expertise. To help novices with animation, we present LogoMotion, an AI code generation approach that helps users create semantically meaningful animation for logos. LogoMotion automatically generates animation code with a method called visually-grounded code synthesis and program repair. This method performs visual analysis, instantiates a design concept, and conducts visual checking to generate animation code. LogoMotion provides novices with code-connected AI editing widgets that help them edit the motion, grouping, and timing of their animation. In a comparison study on 276 animations, LogoMotion was found to produce more content-aware animation than an industry-leading tool. In a user evaluation (n=16) comparing against a prompt-only baseline, these code-connected widgets helped users edit animations with control, iteration, and creative expression.

• The paper introduces LogoMotion, a two-stage system that automates logo animation by generating and refining code based on visual elements.
• It converts logos into structured HTML canvases and groups elements for precise synthesis and visual debugging of animations.
• Quantitative evaluations show that LogoMotion produces more contextually relevant and engaging animations compared to traditional methods.

LogoMotion: Visually Grounded Code Generation for Content-Aware Animation

Introduction to LogoMotion

LogoMotion is a novel system designed to automate the animation of logos by synthesizing animation code tailored to the unique characteristics of each logo design. This system utilizes LLMs to interpret a static logo document and generate dynamic, visually appealing animations. LogoMotion stands out by incorporating a two-stage approach: visually-grounded program synthesis and program repair, which together ensure both the creation and refinement of the animation code based on the visual elements present in the logo.

Visually-Grounded Program Synthesis

LogoMotion operates by first converting a logo into a structured HTML canvas representation. It performs this through a series of steps designed to understand and classify elements of the logo into primary, secondary, text, and background classifications, using a specially trained version of GPT (GPT-4-V). Each element is analyzed for its visual characteristics, such as orientation and conceptual grouping, which informs how these elements should interact in the animation.

The synthesis process follows these main steps:

1. Constructing an HTML Representation of the Canvas: The static elements of a logo are turned into a structured HTML format which captures essential spatial and z-index information.
2. Identifying and Grouping Elements: Elements are classified based on their visual and thematic importance in the logo, and groups are formed among elements that share visual or conceptual characteristics.
3. Designing the Animation Concept: This system generates a 'design concept' for the animation, which ties together the identified elements and their groups into a cohesive animation plan. This plan is used in the next step to generate the actual animation code.

Visually-Grounded Program Repair

After initial code generation, LogoMotion uses a program repair phase to refine the animation. This stage uses visual debugging techniques where the model reviews the animated product against the original static image, identifying discrepancies and bugs like misalignments or incorrect animations.

The process includes:

• Error Identification: The system compares the final frame of the animation with the original logo layout, checking for discrepancies in position, scale, and opacity among animated elements.
• Error Correction: If errors are detected, GPT-4-V is employed again to generate fixes for the noted bugs. This may involve adjusting animation parameters or correcting code snippets responsible for the errors.

Evaluation and Results

Evaluations show that LogoMotion is adept at producing animations that are more aligned with the content and context of the logos compared to traditional animation tools and approaches. In quantitative measures, LogoMotion shows significant improvements in producing context-aware animations when evaluated against standard industry tools.

The key points from the evaluation include:

• Content-Awareness: Animations generated by LogoMotion exhibit a higher degree of relevance to the subject matter of the logos.
• Quality of Animation: While the overall execution quality was comparable to other tools, the unique content-aware capabilities of LogoMotion allowed for more engaging and appropriate animations.

Future Directions and Considerations

Although highly effective, LogoMotion’s current implementation primarily focuses on transform-based attributes (position, scale, opacity). Future enhancements could involve more advanced animation features like morphing, complex color changes, or incorporating physical dynamics, which could further elevate the system’s utility and appeal.

In summary, LogoMotion introduces a powerful, sophisticated approach to automating logo animation, leveraging the capabilities of LLMs not just to generate but also to refine animation code. By doing so, it offers a scalable and user-friendly solution that could potentially transform how designers approach motion in branding and logo design.