MoVer: Motion Verification for Motion Graphics Animations
MoVer employs first-order logic for automated spatio-temporal verification of motion graphics, significantly improving correctness in LLM-based synthesis.
Key Findings
Methodology
This approach integrates large language models (LLMs) for program synthesis and verification, constructing a first-order logic-based DSL called MoVer. It defines predicates for motion attributes and spatial-temporal relations, leveraging Allen’s interval algebra and rectangle algebra. The pipeline involves LLM-generated SVG animations and verification programs, which are executed by a high-performance frame-level engine. The engine evaluates predicates across frames, producing boolean matrices, which are combined via logical operators to identify discrepancies. An iterative feedback loop allows automatic correction of animations, enhancing accuracy. The system is validated on a synthetic dataset of 5600 prompts, demonstrating substantial improvements with multiple iterations.
Key Results
- On a synthetic dataset of 5600 prompts, the pipeline achieved a correct animation generation rate of 58.8% without iteration, which increased to 93.6% after 50 correction cycles. Verification precision exceeded 95%, with false negatives below 10%. The system outperformed rule-based methods in accuracy and efficiency, with verification times averaging a few seconds per animation.
- Ablation studies confirmed that incorporating spatial and temporal predicates based on Allen’s algebra improved verification accuracy by approximately 15%. The high scalability and low latency of the execution engine enabled real-time validation, crucial for iterative refinement.
- The results demonstrate that the combination of logical predicates and LLM program synthesis effectively bridges the gap between natural language prompts and precise motion specifications, enabling robust automatic correction.
Significance
This work addresses a core challenge in automated content creation: ensuring generated motion graphics faithfully reflect complex user specifications. By integrating formal logical verification with LLM-driven synthesis, it offers a scalable, interpretable, and automated solution. This framework enhances the reliability of AI-generated animations, facilitating applications in virtual reality, interactive media, and automated design workflows. It also advances the state-of-the-art in combining symbolic reasoning with deep learning, setting a foundation for future intelligent content verification systems that can handle intricate spatio-temporal relationships with high precision.
Technical Contribution
The paper introduces MoVer, a novel domain-specific language based on first-order logic, capable of expressing detailed spatio-temporal properties. It develops an efficient frame-level SVG execution engine that evaluates predicates in real-time, supporting complex relationships derived from Allen’s interval algebra and rectangle algebra. The integration of LLMs for automatic program synthesis and iterative verification forms a closed-loop system, significantly reducing manual intervention. The creation of a large synthetic dataset for training and evaluation further supports the robustness and scalability of the approach. These innovations collectively push the boundaries of automated verification in motion graphics, combining formal reasoning with deep learning-based program synthesis.
Novelty
This is the first work to embed a comprehensive first-order logic verification framework directly into the motion graphics synthesis pipeline, leveraging LLMs for automatic predicate mapping. Unlike prior methods limited to static or rule-based checks, MoVer dynamically evaluates complex spatio-temporal relationships at frame-level granularity. Its combination of formal logic, high-performance execution, and iterative correction distinguishes it from existing rule-based or purely learning-based approaches, enabling scalable, interpretable, and precise validation of generated animations.
Limitations
- The current implementation is optimized for SVG-based 2D animations, limiting direct application to 3D or non-SVG formats. Extending to more complex representations remains future work.
- Performance may degrade with highly complex scenes involving numerous objects and relations, due to increased computational cost of predicate evaluation.
- Dependence on natural language prompts means that ambiguous or overly complex descriptions can lead to verification inaccuracies, requiring further semantic understanding improvements.
Future Work
Future directions include extending the verification framework to 3D animations and other formats, optimizing the execution engine for large-scale scenes, and integrating multimodal inputs for richer semantic understanding. Additionally, developing user-friendly interfaces for visualizing verification reports and facilitating manual corrections will enhance practical usability. Exploring hybrid approaches combining symbolic reasoning with deep learning for improved semantic parsing and relation detection also holds promise for broader applicability.
AI Executive Summary
In recent years, the rapid development of large language and vision models has revolutionized content creation, enabling automatic generation of images, videos, and animations from natural language prompts. However, ensuring that these AI-generated animations precisely match complex user specifications remains a significant challenge. Existing methods often produce visually appealing results but lack rigorous verification of the detailed spatio-temporal properties described in prompts. This gap limits the reliability and controllability of automated animation systems, especially in professional or industrial contexts where accuracy is critical.
To address this, the paper introduces MoVer, a novel domain-specific language (DSL) based on first-order logic, designed explicitly for verifying motion graphics animations. MoVer defines a comprehensive set of predicates capturing key motion attributes—such as direction, magnitude, and type—and spatial-temporal relationships, including Allen’s interval algebra and rectangle algebra. These predicates enable precise specification and checking of complex relationships like object ordering, relative positioning, and synchronized motions.
The system integrates with large language models (LLMs) to automatically synthesize both animations and verification programs from textual prompts. The pipeline involves LLM-driven program generation, execution of the verification logic on SVG-based animations, and iterative refinement based on verification reports. An efficient frame-level execution engine evaluates predicates across all frames, identifying discrepancies between the generated animation and the specified properties. The feedback loop allows the system to automatically correct errors, significantly improving the correctness rate.
Experimental results on a synthetic dataset of 5600 prompts demonstrate that, without iteration, the system achieves a correctness of 58.8%. After up to 50 correction cycles, this rises to 93.6%, with verification accuracy exceeding 95%. These findings highlight the effectiveness of combining formal logic with deep learning for automated content validation. The approach not only enhances the reliability of AI-generated animations but also provides a scalable, interpretable framework for complex spatio-temporal verification. Looking ahead, extending this framework to 3D animations, optimizing computational performance, and improving semantic understanding are promising directions to further advance automated visual content creation and verification.
Deep Dive
Glossary
First-order logic (一阶逻辑)
一种形式逻辑，用于表达对象、属性和关系的断言，支持量词和逻辑连接词，便于描述复杂关系。
在论文中，作为定义运动和空间关系的基础语言，用于验证动画属性。
Allen’s interval algebra (艾伦时间区间代数)
一种描述时间区间关系的数学框架，包括13种基本关系，如前后、重叠、包含等，用于时序关系推理。
用于定义动画中运动的时间关系，支持验证运动的先后和重叠情况。
Rectangle algebra (矩形代数)
一种空间关系模型，用于描述二维空间中对象的相对位置关系，包括顶部、底部、左、右等关系。
在验证对象空间关系时，用于判断对象的空间位置是否满足描述。
SVG (可缩放矢量图形)
一种基于XML的矢量图形格式，支持逐帧动画和变换，广泛用于网页动画制作。
作为动画的输入和验证的基础表示格式。
LLM (大语言模型)
基于深度学习的自然语言处理模型，能理解和生成复杂文本，支持程序合成和语义理解。
用于从文本提示自动生成动画和验证程序。
Open Questions Unanswered questions from this research
- 1 当前验证框架主要适用于2D SVG动画，如何扩展到复杂的3D场景和非SVG格式仍未解决，未来需研究多模态关系建模。
- 2 在极端复杂场景中，验证的计算成本较高，如何提升效率和可扩展性是关键问题。
- 3 对自然语言描述的理解仍受限，歧义和复杂语义可能影响验证的准确性，需结合更强的语义解析技术。
Applications
Immediate Applications
动画设计自动校验
设计师可以利用系统自动验证动画是否符合描述，减少人工调试，提高效率。
虚拟现实内容生成
在VR场景中自动生成符合空间关系的动画，提升虚拟环境的真实性和交互性。
Long-term Vision
智能内容创作平台
结合验证与生成，打造全流程自动化的虚拟内容制作系统，极大降低人工成本。
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.