# LGTM: Local-to-Global Text-Driven Human Motion Diffusion Model

arXiv: https://arxiv.org/abs/2405.03485 · HTML: https://mlsanity.com/paper/2405.03485/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Placed in Video generation, one of 14 directions in Diffusion models.
- Builds on 18 of its 38 references that are in the map.
- First posted on arXiv 6 May 2024.

## Placed in

- Diffusion models / [Video generation](https://mlsanity.com/diffusion/video-generation/)

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model](https://mlsanity.com/paper/2208.15001/) (2022)
- [Executing your Commands via Motion Diffusion in Latent Space](https://mlsanity.com/paper/2212.04048/) (2022)
- [TEMOS: Generating diverse human motions from textual descriptions](https://mlsanity.com/paper/2204.14109/) (2022)
- [MotionCLIP: Exposing Human Motion Generation to CLIP Space](https://mlsanity.com/paper/2203.08063/) (2022)
- [PhysDiff: Physics-Guided Human Motion Diffusion Model](https://mlsanity.com/paper/2212.02500/) (2022)
- [TM2T: Stochastic and Tokenized Modeling for the Reciprocal Generation of 3D Human Motions and Texts](https://mlsanity.com/paper/2207.01696/) (2022)
- [Language2Pose: Natural Language Grounded Pose Forecasting](https://mlsanity.com/paper/1907.01108/) (2019)
- [TMR: Text-to-Motion Retrieval Using Contrastive 3D Human Motion Synthesis](https://mlsanity.com/paper/2305.00976/) (2023)
- [Listen, Denoise, Action! Audio-Driven Motion Synthesis with Diffusion Models](https://mlsanity.com/paper/2211.09707/) (2022)
- [Synthesis of Compositional Animations from Textual Descriptions](https://mlsanity.com/paper/2103.14675/) (2021)
- [TEACH: Temporal Action Composition for 3D Humans](https://mlsanity.com/paper/2209.04066/) (2022)
- [Motion Puzzle: Arbitrary Motion Style Transfer by Body Part](https://mlsanity.com/paper/2202.05274/) (2022)
- [MoConVQ: Unified Physics-Based Motion Control via Scalable Discrete Representations](https://mlsanity.com/paper/2310.10198/) (2023)
- [PMP: Learning to Physically Interact with Environments using Part-wise Motion Priors](https://mlsanity.com/paper/2305.03249/) (2023)
- [ChatGPT for Digital Forensic Investigation: The Good, The Bad, and The Unknown](https://mlsanity.com/paper/2307.10195/) (2023)

## Built on by (most cited first, 18 of 18)

- [MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm](https://mlsanity.com/paper/2502.02358/) (2025)
- [KinMo: Kinematic-aware Human Motion Understanding and Generation](https://mlsanity.com/paper/2411.15472/) (2024)
- [Dynamic Motion Blending for Versatile Motion Editing](https://mlsanity.com/paper/2503.20724/) (2025)
- [Multi-Person Interaction Generation from Two-Person Motion Priors](https://mlsanity.com/paper/2505.17860/) (2025)
- [BiPO: Bidirectional Partial Occlusion Network for Text-to-Motion Synthesis](https://mlsanity.com/paper/2412.00112/) (2024)
- [PP-Motion: Physical-Perceptual Fidelity Evaluation for Human Motion Generation](https://mlsanity.com/paper/2508.08179/) (2025)
- [Beyond Global Alignment: Fine-Grained Motion-Language Retrieval via Pyramidal Shapley-Taylor Learning](https://mlsanity.com/paper/2601.21904/) (2026)
- [Spatial-Temporal Multi-Scale Quantization for Flexible Motion Generation](https://mlsanity.com/paper/2508.08991/) (2025)
- [Toward Rich Video Human-Motion2D Generation](https://mlsanity.com/paper/2506.14428/) (2025)
- [Stable Signer: Hierarchical Sign Language Generative Model](https://mlsanity.com/paper/2512.04048/) (2025)
- [Topology-Agnostic Animal Motion Generation from Text Prompt](https://mlsanity.com/paper/2512.10352/) (2025)
- [ParTY: Part-Guidance for Expressive Text-to-Motion Synthesis](https://mlsanity.com/paper/2603.09611/) (2026)
- [InfiniteDance: Scalable 3D Dance Generation Towards in-the-wild Generalization](https://mlsanity.com/paper/2603.13375/) (2026)
- [TopoCap: Learning Topology-Agnostic Motion Priors for Monocular Video-to-Animation](https://mlsanity.com/paper/2606.12153/) (2026)
- [Step2Motion: Locomotion Reconstruction from Pressure Sensing Insoles](https://mlsanity.com/paper/2510.22712/) (2025)
- [Listen to Rhythm, Choose Movements: Autoregressive Multimodal Dance Generation via Diffusion and Mamba with Decoupled Dance Dataset](https://mlsanity.com/paper/2601.03323/) (2026)
- [Discovering High Level Patterns from Simulation Traces](https://mlsanity.com/paper/2602.10009/) (2026)
- [Streaming Multi-Track Timeline Control for 3D Human Motion Generation](https://mlsanity.com/paper/2610.03873/) (2026)

## Abstract (arXiv)

In this paper, we introduce LGTM, a novel Local-to-Global pipeline for Text-to-Motion generation. LGTM utilizes a diffusion-based architecture and aims to address the challenge of accurately translating textual descriptions into semantically coherent human motion in computer animation. Specifically, traditional methods often struggle with semantic discrepancies, particularly in aligning specific motions to the correct body parts. To address this issue, we propose a two-stage pipeline to overcome this challenge: it first employs large language models (LLMs) to decompose global motion descriptions into part-specific narratives, which are then processed by independent body-part motion encoders to ensure precise local semantic alignment. Finally, an attention-based full-body optimizer refines the motion generation results and guarantees the overall coherence. Our experiments demonstrate that LGTM gains significant improvements in generating locally accurate, semantically-aligned human motion, marking a notable advancement in text-to-motion applications. Code and data for this paper are available at https://github.com/L-Sun/LGTM