MoVer: Motion Verification for Motion Graphics Animations

TL;DR

MoVer introduces a logic-based verification system for motion graphics animations, enabling iterative refinement of animations generated from text prompts using an LLM pipeline.

Contribution

We develop MoVer, a first-order logic-based DSL for verifying spatio-temporal properties of motion graphics, integrated into an LLM-driven synthesis and correction pipeline.

Findings

Correct animations generated for 58.8% of prompts without iteration

Iterative correction improves accuracy to 93.6%

Built a synthetic dataset of 5600 prompt-annotation pairs

Abstract

While large vision-language models can generate motion graphics animations from text prompts, they regularly fail to include all spatio-temporal properties described in the prompt. We introduce MoVer, a motion verification DSL based on first-order logic that can check spatio-temporal properties of a motion graphics animation. We identify a general set of such properties that people commonly use to describe animations (e.g., the direction and timing of motions, the relative positioning of objects, etc.). We implement these properties as predicates in MoVer and provide an execution engine that can apply a MoVer program to any input SVG-based motion graphics animation. We then demonstrate how MoVer can be used in an LLM-based synthesis and verification pipeline for iteratively refining motion graphics animations. Given a text prompt, our pipeline synthesizes a motion graphics animation and a corresponding MoVer program. Executing the verification program on the animation yields a report of the predicates that failed and the report can be automatically fed back to LLM to iteratively correct the animation. To evaluate our pipeline, we build a synthetic dataset of 5600 text prompts paired with ground truth MoVer verification programs. We find that while our LLM-based pipeline is able to automatically generate a correct motion graphics animation for 58.8% of the test prompts without any iteration, this number raises to 93.6% with up to 50 correction iterations. Our code and dataset are at https://mover-dsl.github.io.