# Structure-Aware Human-Action Generation

arXiv: https://arxiv.org/abs/2007.01971 · HTML: https://mlsanity.com/paper/2007.01971/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jul 2020.

## Built on by (most cited first, 28 of 28)

- [TM2T: Stochastic and Tokenized Modeling for the Reciprocal Generation of 3D Human Motions and Texts](https://mlsanity.com/paper/2207.01696/) (2022)
- [Motion-X: A Large-scale 3D Expressive Whole-body Human Motion Dataset](https://mlsanity.com/paper/2307.00818/) (2023)
- [HumanTOMATO: Text-aligned Whole-body Motion Generation](https://mlsanity.com/paper/2310.12978/) (2023)
- [Human Motion Generation: A Survey](https://mlsanity.com/paper/2307.10894/) (2023)
- [Single Motion Diffusion](https://mlsanity.com/paper/2302.05905/) (2023)
- [MoDi: Unconditional Motion Synthesis from Diverse Data](https://mlsanity.com/paper/2206.08010/) (2022)
- [WANDR: Intention-guided Human Motion Generation](https://mlsanity.com/paper/2404.15383/) (2024)
- [Generative Adversarial Graph Convolutional Networks for Human Action Synthesis](https://mlsanity.com/paper/2110.11191/) (2021)
- [FLAG3D: A 3D Fitness Activity Dataset with Language Instruction](https://mlsanity.com/paper/2212.04638/) (2022)
- [GAN-based Reactive Motion Synthesis with Class-aware Discriminators for Human-human Interaction](https://mlsanity.com/paper/2110.00380/) (2021)
- [Language-guided Human Motion Synthesis with Atomic Actions](https://mlsanity.com/paper/2308.09611/) (2023)
- [MUGL: Large Scale Multi Person Conditional Action Generation with Locomotion](https://mlsanity.com/paper/2110.11460/) (2021)
- [StickMotion: Generating 3D Human Motions by Drawing a Stickman](https://mlsanity.com/paper/2503.04829/) (2025)
- [PaCMO: Partner Dependent Human Motion Generation in Dyadic Human Activity using Neural Operators](https://mlsanity.com/paper/2211.16210/) (2022)
- [Text-driven Motion Generation: Overview, Challenges and Directions](https://mlsanity.com/paper/2505.09379/) (2025)
- [Motion Generation: A Survey of Generative Approaches and Benchmarks](https://mlsanity.com/paper/2507.05419/) (2025)
- [Skeleton2Humanoid: Animating Simulated Characters for Physically-plausible Motion In-betweening](https://mlsanity.com/paper/2210.04294/) (2022)
- [Token Boosting for Robust Self-Supervised Visual Transformer Pre-training](https://mlsanity.com/paper/2304.04175/) (2023)
- [MPE4G: Multimodal Pretrained Encoder for Co-Speech Gesture Generation](https://mlsanity.com/paper/2305.15740/) (2023)
- [SIGHT: Synthesizing Image-Text Conditioned and Geometry-Guided 3D Hand-Object Trajectories](https://mlsanity.com/paper/2503.22869/) (2025)
- [PlanMoGPT: Flow-Enhanced Progressive Planning for Text to Motion Synthesis](https://mlsanity.com/paper/2506.17912/) (2025)
- [SignSparK: Efficient Multilingual Sign Language Production via Sparse Keyframe Learning](https://mlsanity.com/paper/2603.10446/) (2026)
- [Riemannian Motion Generation: A Unified Framework for Human Motion Representation and Generation via Riemannian Flow Matching](https://mlsanity.com/paper/2603.15016/) (2026)
- [3d human motion generation from the text via gesture action classification and the autoregressive model](https://mlsanity.com/paper/2211.10003/) (2022)
- [Motion Generation Review: Exploring Deep Learning for Lifelike Animation with Manifold](https://mlsanity.com/paper/2412.10458/) (2024)
- [TextIM: Part-aware Interactive Motion Synthesis from Text](https://mlsanity.com/paper/2408.03302/) (2024)
- [Generative AI-Driven High-Fidelity Human Motion Simulation](https://mlsanity.com/paper/2507.14097/) (2025)
- [DrawMotion: Generating 3D Human Motions by Freehand Drawing](https://mlsanity.com/paper/2605.20955/) (2026)

## Abstract (arXiv)

Generating long-range skeleton-based human actions has been a challenging problem since small deviations of one frame can cause a malformed action sequence. Most existing methods borrow ideas from video generation, which naively treat skeleton nodes/joints as pixels of images without considering the rich inter-frame and intra-frame structure information, leading to potential distorted actions. Graph convolutional networks (GCNs) is a promising way to leverage structure information to learn structure representations. However, directly adopting GCNs to tackle such continuous action sequences both in spatial and temporal spaces is challenging as the action graph could be huge. To overcome this issue, we propose a variant of GCNs to leverage the powerful self-attention mechanism to adaptively sparsify a complete action graph in the temporal space. Our method could dynamically attend to important past frames and construct a sparse graph to apply in the GCN framework, well-capturing the structure information in action sequences. Extensive experimental results demonstrate the superiority of our method on two standard human action datasets compared with existing methods.