LaMoGen: Language to Motion Generation Through LLM-Guided Symbolic Inference

2026-03-12

AI Summary

Existing text-to-motion generation methods rely on end-to-end embeddings, struggling to simultaneously achieve temporal precision, fine-grained detail, and interpretability. This work proposes LaMoGen, a novel framework that introduces LabanLite—a lightweight symbolic system derived from Labanotation—and establishes a three-stage “text–symbol–motion” generation pipeline. For the first time, large language models (LLMs) are leveraged at the symbolic level to perform motion reasoning and recombination. This approach enables an interpretable mapping between language and motion, significantly outperforming state-of-the-art methods on both a newly curated Labanotation benchmark and two public datasets. The proposed method achieves substantial improvements in motion interpretability, controllability, and alignment between textual descriptions and generated motions.

Technology Category

• Natural Language Processing: Generation
• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Motion & Tracking

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Human motion is highly expressive and naturally aligned with language, yet prevailing methods relying heavily on joint text-motion embeddings struggle to synthesize temporally accurate, detailed motions and often lack explainability. To address these limitations, we introduce LabanLite, a motion representation developed by adapting and extending the Labanotation system. Unlike black-box text-motion embeddings, LabanLite encodes each atomic body-part action (e.g., a single left-foot step) as a discrete Laban symbol paired with a textual template. This abstraction decomposes complex motions into interpretable symbol sequences and body-part instructions, establishing a symbolic link between high-level language and low-level motion trajectories. Building on LabanLite, we present LaMoGen, a Text-to-LabanLite-to-Motion Generation framework that enables large language models (LLMs) to compose motion sequences through symbolic reasoning. The LLM interprets motion patterns, relates them to textual descriptions, and recombines symbols into executable plans, producing motions that are both interpretable and linguistically grounded. To support rigorous evaluation, we introduce a Labanotation-based benchmark with structured description-motion pairs and three metrics that jointly measure text-motion alignment across symbolic, temporal, and harmony dimensions. Experiments demonstrate that LaMoGen establishes a new baseline for both interpretability and controllability, outperforming prior methods on our benchmark and two public datasets. These results highlight the advantages of symbolic reasoning and agent-based design for language-driven motion synthesis.

Problem

• text-to-motion generation
• motion interpretability
• temporal accuracy
• language-motion alignment
• symbolic representation

Innovation

• symbolic reasoning
• Labanotation
• interpretable motion generation
• LLM-guided generation
• text-to-motion synthesis