# MoVer: Motion Verification for Motion Graphics Animations

arXiv: https://arxiv.org/abs/2502.13372 · HTML: https://mlsanity.com/paper/2502.13372/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Placed in Video understanding with MLLMs, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 31 of its 90 references that are in the map.
- First posted on arXiv 19 Feb 2025.

## Placed in

- Multimodal LLMs / vision-language models / [Video understanding with MLLMs](https://mlsanity.com/mllm/video-understanding-with-mllms/)

## Builds on (most cited first, 30 of 31)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [CLIPScore: A Reference-free Evaluation Metric for Image Captioning](https://mlsanity.com/paper/2104.08718/) (2021)
- [AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning](https://mlsanity.com/paper/2307.04725/) (2023)
- [ViperGPT: Visual Inference via Python Execution for Reasoning](https://mlsanity.com/paper/2303.08128/) (2023)
- [VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models](https://mlsanity.com/paper/2401.09047/) (2024)
- [Visual Programming: Compositional visual reasoning without training](https://mlsanity.com/paper/2211.11559/) (2022)
- [TIFA: Accurate and Interpretable Text-to-Image Faithfulness Evaluation with Question Answering](https://mlsanity.com/paper/2303.11897/) (2023)
- [SparseCtrl: Adding Sparse Controls to Text-to-Video Diffusion Models](https://mlsanity.com/paper/2311.16933/) (2023)
- [Davidsonian Scene Graph: Improving Reliability in Fine-grained Evaluation for Text-to-Image Generation](https://mlsanity.com/paper/2310.18235/) (2023)
- [Efficient Few-Shot Learning Without Prompts](https://mlsanity.com/paper/2209.11055/) (2022)
- [SceneCraft: An LLM Agent for Synthesizing 3D Scene as Blender Code](https://mlsanity.com/paper/2403.01248/) (2024)
- [Inferring and Executing Programs for Visual Reasoning](https://mlsanity.com/paper/1705.03633/) (2017)
- [ToonCrafter: Generative Cartoon Interpolation](https://mlsanity.com/paper/2405.17933/) (2024)
- [Empowering LLMs to Understand and Generate Complex Vector Graphics](https://mlsanity.com/paper/2412.11102/) (2024)
- [A Survey on Semantic Parsing](https://mlsanity.com/paper/1812.00978/) (2018)
- [Breathing Life Into Sketches Using Text-to-Video Priors](https://mlsanity.com/paper/2311.13608/) (2023)
- [Iterative Motion Editing with Natural Language](https://mlsanity.com/paper/2312.11538/) (2023)
- [LLMR: Real-time Prompting of Interactive Worlds using Large Language Models](https://mlsanity.com/paper/2309.12276/) (2023)
- [The Scene Language: Representing Scenes with Programs, Words, and Embeddings](https://mlsanity.com/paper/2410.16770/) (2024)
- [What's Left? Concept Grounding with Logic-Enhanced Foundation Models](https://mlsanity.com/paper/2310.16035/) (2023)
- [Programmatically Grounded, Compositionally Generalizable Robotic Manipulation](https://mlsanity.com/paper/2304.13826/) (2023)
- [F-coref: Fast, Accurate and Easy to Use Coreference Resolution](https://mlsanity.com/paper/2209.04280/) (2022)
- [Text2CAD: Generating Sequential CAD Models from Beginner-to-Expert Level Text Prompts](https://mlsanity.com/paper/2409.17106/) (2024)
- [NeuralSVG: An Implicit Representation for Text-to-Vector Generation](https://mlsanity.com/paper/2501.03992/) (2025)
- [Predicated Diffusion: Predicate Logic-Based Attention Guidance for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2311.16117/) (2023)
- [LogoMotion: Visually-Grounded Code Synthesis for Creating and Editing Animation](https://mlsanity.com/paper/2405.07065/) (2024)
- [A Solver-Aided Hierarchical Language for LLM-Driven CAD Design](https://mlsanity.com/paper/2502.09819/) (2025)
- [Keyframer: Empowering Animation Design using Large Language Models](https://mlsanity.com/paper/2402.06071/) (2024)
- [Editing Motion Graphics Video via Motion Vectorization and Transformation](https://mlsanity.com/paper/2309.14642/) (2023)

## Built on by (most cited first, 10 of 10)

- [How Do AI Agents Do Human Work? Comparing AI and Human Workflows Across Diverse Occupations](https://mlsanity.com/paper/2510.22780/) (2025)
- [LayerPeeler: Autoregressive Peeling for Layer-wise Image Vectorization](https://mlsanity.com/paper/2505.23740/) (2025)
- [LottieGPT: Tokenizing Vector Animation for Autoregressive Generation](https://mlsanity.com/paper/2604.11792/) (2026)
- [AnimAgents: Coordinating Multi-Stage Animation Pre-Production with Human-Multi-Agent Collaboration](https://mlsanity.com/paper/2511.17906/) (2025)
- [Self-Consistency for LLM-Based Motion Trajectory Generation and Verification](https://mlsanity.com/paper/2603.29301/) (2026)
- [From Static to Interactive: Authoring Interactive Visualizations via Natural Language](https://mlsanity.com/paper/2601.17736/) (2026)
- [Discovering High Level Patterns from Simulation Traces](https://mlsanity.com/paper/2602.10009/) (2026)
- [SceneTeract: Probing and Improving Agent-Aware Activity Reasoning in 3D Indoor Scenes](https://mlsanity.com/paper/2603.29798/) (2026)
- [The Garden of Forking Paths: Threading Narrative Archetype as a Semantic Signal Through Gameplay Planning](https://mlsanity.com/paper/2605.01245/) (2026)
- [Spatula: Exploring On-Demand In-Situ Interfaces and Interaction for Attribute Control](https://mlsanity.com/paper/2607.10405/) (2026)

## Abstract (arXiv)

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.