# Language-guided Human Motion Synthesis with Atomic Actions

arXiv: https://arxiv.org/abs/2308.09611 · HTML: https://mlsanity.com/paper/2308.09611/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 41 of its 59 references that are in the map.
- First posted on arXiv 18 Aug 2023.

## Builds on (most cited first, 30 of 41)

- [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)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [AMASS: Archive of Motion Capture as Surface Shapes](https://mlsanity.com/paper/1904.03278/) (2019)
- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [Human Motion Diffusion Model](https://mlsanity.com/paper/2209.14916/) (2022)
- [StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery](https://mlsanity.com/paper/2103.17249/) (2021)
- [MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model](https://mlsanity.com/paper/2208.15001/) (2022)
- [Generative Adversarial Text to Image Synthesis](https://mlsanity.com/paper/1605.05396/) (2016)
- [NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis](https://mlsanity.com/paper/1604.02808/) (2016)
- [StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks](https://mlsanity.com/paper/1612.03242/) (2016)
- [NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding](https://mlsanity.com/paper/1905.04757/) (2019)
- [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)
- [Unicoder-VL: A Universal Encoder for Vision and Language by Cross-modal Pre-training](https://mlsanity.com/paper/1908.06066/) (2019)
- [Action2Motion: Conditioned Generation of 3D Human Motions](https://mlsanity.com/paper/2007.15240/) (2020)
- [MotionCLIP: Exposing Human Motion Generation to CLIP Space](https://mlsanity.com/paper/2203.08063/) (2022)
- [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)
- [TEACH: Temporal Action Composition for 3D Humans](https://mlsanity.com/paper/2209.04066/) (2022)
- [HUMANISE: Language-conditioned Human Motion Generation in 3D Scenes](https://mlsanity.com/paper/2210.09729/) (2022)
- [ManiGAN: Text-Guided Image Manipulation](https://mlsanity.com/paper/1912.06203/) (2019)
- [Synthesizing Long-Term 3D Human Motion and Interaction in 3D Scenes](https://mlsanity.com/paper/2012.05522/) (2020)
- [Text2Gestures: A Transformer-Based Network for Generating Emotive Body Gestures for Virtual Agents](https://mlsanity.com/paper/2101.11101/) (2021)

## Built on by (most cited first, 17 of 17)

- [Large Motion Model for Unified Multi-Modal Motion Generation](https://mlsanity.com/paper/2404.01284/) (2024)
- [HOIGPT: Learning Long Sequence Hand-Object Interaction with Language Models](https://mlsanity.com/paper/2503.19157/) (2025)
- [Towards Open Domain Text-Driven Synthesis of Multi-Person Motions](https://mlsanity.com/paper/2405.18483/) (2024)
- [Advancing Social Intelligence in AI Agents: Technical Challenges and Open Questions](https://mlsanity.com/paper/2404.11023/) (2024)
- [Text-controlled Motion Mamba: Text-Instructed Temporal Grounding of Human Motion](https://mlsanity.com/paper/2404.11375/) (2024)
- [SignAvatar: Sign Language 3D Motion Reconstruction and Generation](https://mlsanity.com/paper/2405.07974/) (2024)
- [Local Action-Guided Motion Diffusion Model for Text-to-Motion Generation](https://mlsanity.com/paper/2407.10528/) (2024)
- [Spectrum AUC Difference (SAUCD): Human-aligned 3D Shape Evaluation](https://mlsanity.com/paper/2403.01619/) (2024)
- [Text-driven Motion Generation: Overview, Challenges and Directions](https://mlsanity.com/paper/2505.09379/) (2025)
- [Ig3D: Integrating 3D Face Representations in Facial Expression Inference](https://mlsanity.com/paper/2408.16907/) (2024)
- [PP-Motion: Physical-Perceptual Fidelity Evaluation for Human Motion Generation](https://mlsanity.com/paper/2508.08179/) (2025)
- [Divide and Fuse: Body Part Mesh Recovery from Partially Visible Human Images](https://mlsanity.com/paper/2407.09694/) (2024)
- [Language-Guided Transformer Tokenizer for Human Motion Generation](https://mlsanity.com/paper/2602.08337/) (2026)
- [SRAM: Shape-Realism Alignment Metric for No Reference 3D Shape Evaluation](https://mlsanity.com/paper/2512.01373/) (2025)
- [Textured Geometry Evaluation: Perceptual 3D Textured Shape Metric via 3D Latent-Geometry Network](https://mlsanity.com/paper/2512.01380/) (2025)
- [DeMoGen: Towards Decompositional Human Motion Generation with Energy-Based Diffusion Models](https://mlsanity.com/paper/2512.22324/) (2025)
- [ConfusionBench: An Expert-Validated Benchmark for Confusion Recognition and Localization in Educational Videos](https://mlsanity.com/paper/2603.17267/) (2026)

## Abstract (arXiv)

Language-guided human motion synthesis has been a challenging task due to the inherent complexity and diversity of human behaviors. Previous methods face limitations in generalization to novel actions, often resulting in unrealistic or incoherent motion sequences. In this paper, we propose ATOM (ATomic mOtion Modeling) to mitigate this problem, by decomposing actions into atomic actions, and employing a curriculum learning strategy to learn atomic action composition. First, we disentangle complex human motions into a set of atomic actions during learning, and then assemble novel actions using the learned atomic actions, which offers better adaptability to new actions. Moreover, we introduce a curriculum learning training strategy that leverages masked motion modeling with a gradual increase in the mask ratio, and thus facilitates atomic action assembly. This approach mitigates the overfitting problem commonly encountered in previous methods while enforcing the model to learn better motion representations. We demonstrate the effectiveness of ATOM through extensive experiments, including text-to-motion and action-to-motion synthesis tasks. We further illustrate its superiority in synthesizing plausible and coherent text-guided human motion sequences.