# Fg-T2M: Fine-Grained Text-Driven Human Motion Generation via Diffusion Model

arXiv: https://arxiv.org/abs/2309.06284 · HTML: https://mlsanity.com/paper/2309.06284/

## Key facts

- Cited by 79 papers in the map (data through 14 Oct 2026).
- Placed in Video generation, one of 14 directions in Diffusion models.
- Builds on 25 of its 34 references that are in the map.
- First posted on arXiv 12 Sep 2023.

## Placed in

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

## Builds on (most cited first, 25 of 25)

- [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)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [AMASS: Archive of Motion Capture as Surface Shapes](https://mlsanity.com/paper/1904.03278/) (2019)
- [Human Motion Diffusion Model](https://mlsanity.com/paper/2209.14916/) (2022)
- [MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model](https://mlsanity.com/paper/2208.15001/) (2022)
- [T2M-GPT: Generating Human Motion from Textual Descriptions with Discrete Representations](https://mlsanity.com/paper/2301.06052/) (2023)
- [MoCoGAN: Decomposing Motion and Content for Video Generation](https://mlsanity.com/paper/1707.04993/) (2017)
- [Action-Conditioned 3D Human Motion Synthesis with Transformer VAE](https://mlsanity.com/paper/2104.05670/) (2021)
- [TEMOS: Generating diverse human motions from textual descriptions](https://mlsanity.com/paper/2204.14109/) (2022)
- [AI Choreographer: Music Conditioned 3D Dance Generation with AIST++](https://mlsanity.com/paper/2101.08779/) (2021)
- [Action2Motion: Conditioned Generation of 3D Human Motions](https://mlsanity.com/paper/2007.15240/) (2020)
- [TM2T: Stochastic and Tokenized Modeling for the Reciprocal Generation of 3D Human Motions and Texts](https://mlsanity.com/paper/2207.01696/) (2022)
- [The KIT Motion-Language Dataset](https://mlsanity.com/paper/1607.03827/) (2016)
- [Language2Pose: Natural Language Grounded Pose Forecasting](https://mlsanity.com/paper/1907.01108/) (2019)
- [Synthesis of Compositional Animations from Textual Descriptions](https://mlsanity.com/paper/2103.14675/) (2021)
- [Text2Action: Generative Adversarial Synthesis from Language to Action](https://mlsanity.com/paper/1710.05298/) (2017)
- [Text2Gestures: A Transformer-Based Network for Generating Emotive Body Gestures for Virtual Agents](https://mlsanity.com/paper/2101.11101/) (2021)
- [Learning a bidirectional mapping between human whole-body motion and natural language using deep recurrent neural networks](https://mlsanity.com/paper/1705.06400/) (2017)
- [Implicit Neural Representations for Variable Length Human Motion Generation](https://mlsanity.com/paper/2203.13694/) (2022)
- [Temporally Guided Music-to-Body-Movement Generation](https://mlsanity.com/paper/2009.08015/) (2020)

## Built on by (most cited first, 30 of 79)

- [CG-HOI: Contact-Guided 3D Human-Object Interaction Generation](https://mlsanity.com/paper/2311.16097/) (2023)
- [MMM: Generative Masked Motion Model](https://mlsanity.com/paper/2312.03596/) (2023)
- [MotionStreamer: Streaming Motion Generation via Diffusion-based Autoregressive Model in Causal Latent Space](https://mlsanity.com/paper/2503.15451/) (2025)
- [Large Motion Model for Unified Multi-Modal Motion Generation](https://mlsanity.com/paper/2404.01284/) (2024)
- [LaMP: Language-Motion Pretraining for Motion Generation, Retrieval, and Captioning](https://mlsanity.com/paper/2410.07093/) (2024)
- [Taming Diffusion Probabilistic Models for Character Control](https://mlsanity.com/paper/2404.15121/) (2024)
- [ParCo: Part-Coordinating Text-to-Motion Synthesis](https://mlsanity.com/paper/2403.18512/) (2024)
- [BAMM: Bidirectional Autoregressive Motion Model](https://mlsanity.com/paper/2403.19435/) (2024)
- [StableMoFusion: Towards Robust and Efficient Diffusion-based Motion Generation Framework](https://mlsanity.com/paper/2405.05691/) (2024)
- [RoHM: Robust Human Motion Reconstruction via Diffusion](https://mlsanity.com/paper/2401.08570/) (2024)
- [Motion-Agent: A Conversational Framework for Human Motion Generation with LLMs](https://mlsanity.com/paper/2405.17013/) (2024)
- [THOR: Text to Human-Object Interaction Diffusion via Relation Intervention](https://mlsanity.com/paper/2403.11208/) (2024)
- [Digital Life Project: Autonomous 3D Characters with Social Intelligence](https://mlsanity.com/paper/2312.04547/) (2023)
- [Motion Anything: Any to Motion Generation](https://mlsanity.com/paper/2503.06955/) (2025)
- [MoGenTS: Motion Generation based on Spatial-Temporal Joint Modeling](https://mlsanity.com/paper/2409.17686/) (2024)
- [SALAD: Skeleton-aware Latent Diffusion for Text-driven Motion Generation and Editing](https://mlsanity.com/paper/2503.13836/) (2025)
- [HOIGPT: Learning Long Sequence Hand-Object Interaction with Language Models](https://mlsanity.com/paper/2503.19157/) (2025)
- [Light-T2M: A Lightweight and Fast Model for Text-to-motion Generation](https://mlsanity.com/paper/2412.11193/) (2024)
- [MotionScript: Natural Language Descriptions for Expressive 3D Human Motions](https://mlsanity.com/paper/2312.12634/) (2023)
- [Fg-T2M++: LLMs-Augmented Fine-Grained Text Driven Human Motion Generation](https://mlsanity.com/paper/2502.05534/) (2025)
- [SATO: Stable Text-to-Motion Framework](https://mlsanity.com/paper/2405.01461/) (2024)
- [ChainHOI: Joint-based Kinematic Chain Modeling for Human-Object Interaction Generation](https://mlsanity.com/paper/2503.13130/) (2025)
- [DisCoRD: Discrete Tokens to Continuous Motion via Rectified Flow Decoding](https://mlsanity.com/paper/2411.19527/) (2024)
- [CoMA: Compositional Human Motion Generation with Multi-modal Agents](https://mlsanity.com/paper/2412.07320/) (2024)
- [Realistic Human Motion Generation with Cross-Diffusion Models](https://mlsanity.com/paper/2312.10993/) (2023)
- [Tri-Modal Motion Retrieval by Learning a Joint Embedding Space](https://mlsanity.com/paper/2403.00691/) (2024)
- [Text-controlled Motion Mamba: Text-Instructed Temporal Grounding of Human Motion](https://mlsanity.com/paper/2404.11375/) (2024)
- [Language-Driven 6-DoF Grasp Detection Using Negative Prompt Guidance](https://mlsanity.com/paper/2407.13842/) (2024)
- [SoPo: Text-to-Motion Generation Using Semi-Online Preference Optimization](https://mlsanity.com/paper/2412.05095/) (2024)
- [MVPortrait: Text-Guided Motion and Emotion Control for Multi-view Vivid Portrait Animation](https://mlsanity.com/paper/2503.19383/) (2025)

## Abstract (arXiv)

Text-driven human motion generation in computer vision is both significant and challenging. However, current methods are limited to producing either deterministic or imprecise motion sequences, failing to effectively control the temporal and spatial relationships required to conform to a given text description. In this work, we propose a fine-grained method for generating high-quality, conditional human motion sequences supporting precise text description. Our approach consists of two key components: 1) a linguistics-structure assisted module that constructs accurate and complete language feature to fully utilize text information; and 2) a context-aware progressive reasoning module that learns neighborhood and overall semantic linguistics features from shallow and deep graph neural networks to achieve a multi-step inference. Experiments show that our approach outperforms text-driven motion generation methods on HumanML3D and KIT test sets and generates better visually confirmed motion to the text conditions.