# HINT: Hierarchical Interaction Modeling for Autoregressive Multi-Human Motion Generation

arXiv: https://arxiv.org/abs/2601.20383 · HTML: https://mlsanity.com/paper/2601.20383/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 31 references that are in the map.
- First posted on arXiv 28 Jan 2026.

## Builds on (most cited first, 28 of 28)

- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [On the Continuity of Rotation Representations in Neural Networks](https://mlsanity.com/paper/1812.07035/) (2018)
- [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)
- [Executing your Commands via Motion Diffusion in Latent Space](https://mlsanity.com/paper/2212.04048/) (2022)
- [MotionGPT: Human Motion as a Foreign Language](https://mlsanity.com/paper/2306.14795/) (2023)
- [TEMOS: Generating diverse human motions from textual descriptions](https://mlsanity.com/paper/2204.14109/) (2022)
- [Human Motion Diffusion as a Generative Prior](https://mlsanity.com/paper/2303.01418/) (2023)
- [InterGen: Diffusion-based Multi-human Motion Generation under Complex Interactions](https://mlsanity.com/paper/2304.05684/) (2023)
- [Listen, Denoise, Action! Audio-Driven Motion Synthesis with Diffusion Models](https://mlsanity.com/paper/2211.09707/) (2022)
- [InterDiff: Generating 3D Human-Object Interactions with Physics-Informed Diffusion](https://mlsanity.com/paper/2308.16905/) (2023)
- [Inter-X: Towards Versatile Human-Human Interaction Analysis](https://mlsanity.com/paper/2312.16051/) (2023)
- [DartControl: A Diffusion-Based Autoregressive Motion Model for Real-Time Text-Driven Motion Control](https://mlsanity.com/paper/2410.05260/) (2024)
- [Seamless Human Motion Composition with Blended Positional Encodings](https://mlsanity.com/paper/2402.15509/) (2024)
- [ReMoS: 3D Motion-Conditioned Reaction Synthesis for Two-Person Interactions](https://mlsanity.com/paper/2311.17057/) (2023)
- [Taming Diffusion Probabilistic Models for Character Control](https://mlsanity.com/paper/2404.15121/) (2024)
- [ReGenNet: Towards Human Action-Reaction Synthesis](https://mlsanity.com/paper/2403.11882/) (2024)
- [InterMask: 3D Human Interaction Generation via Collaborative Masked Modeling](https://mlsanity.com/paper/2410.10010/) (2024)
- [Digital Life Project: Autonomous 3D Characters with Social Intelligence](https://mlsanity.com/paper/2312.04547/) (2023)
- [Interaction Transformer for Human Reaction Generation](https://mlsanity.com/paper/2207.01685/) (2022)
- [in2IN: Leveraging individual Information to Generate Human INteractions](https://mlsanity.com/paper/2404.09988/) (2024)
- [InterControl: Zero-shot Human Interaction Generation by Controlling Every Joint](https://mlsanity.com/paper/2311.15864/) (2023)
- [PhysReaction: Physically Plausible Real-Time Humanoid Reaction Synthesis via Forward Dynamics Guided 4D Imitation](https://mlsanity.com/paper/2404.01081/) (2024)
- [SocialGen: Modeling Multi-Human Social Interaction with Language Models](https://mlsanity.com/paper/2503.22906/) (2025)
- [PINO: Person-Interaction Noise Optimization for Long-Duration and Customizable Motion Generation of Arbitrary-Sized Groups](https://mlsanity.com/paper/2507.19292/) (2025)
- [Text-driven Motion Generation: Overview, Challenges and Directions](https://mlsanity.com/paper/2505.09379/) (2025)
- [Multi-Person Interaction Generation from Two-Person Motion Priors](https://mlsanity.com/paper/2505.17860/) (2025)

## Built on by (most cited first, 4 of 4)

- [InterEdit: Navigating Text-Guided 3D Dyadic Human Motion Editing](https://mlsanity.com/paper/2603.13082/) (2026)
- [InterCMDM: Block-Causal Diffusion for Autoregressive Human Interaction Generation](https://mlsanity.com/paper/2607.01743/) (2026)
- [ARMS: Anchor-Relational Motion Streaming for Seamless Solo-Social Motion Transitions](https://mlsanity.com/paper/2607.05733/) (2026)
- [From Digital Human Interactions to Physics-Based Humanoid Skills: Physics-Grounded Post-Training of Interaction Generators](https://mlsanity.com/paper/2610.10322/) (2026)

## Abstract (arXiv)

Text-driven multi-human motion generation with complex interactions remains a challenging problem. Despite progress in performance, existing offline methods that generate fixed-length motions with a fixed number of agents, are inherently limited in handling long or variable text, and varying agent counts. These limitations naturally encourage autoregressive formulations, which predict future motions step by step conditioned on all past trajectories and current text guidance. In this work, we introduce HINT, the first autoregressive framework for multi-human motion generation with Hierarchical INTeraction modeling in diffusion. First, HINT leverages a disentangled motion representation within a canonicalized latent space, decoupling local motion semantics from inter-person interactions. This design facilitates direct adaptation to varying numbers of human participants without requiring additional refinement. Second, HINT adopts a sliding-window strategy for efficient online generation, and aggregates local within-window and global cross-window conditions to capture past human history, inter-person dependencies, and align with text guidance. This strategy not only enables fine-grained interaction modeling within each window but also preserves long-horizon coherence across all the long sequence. Extensive experiments on public benchmarks demonstrate that HINT matches the performance of strong offline models and surpasses autoregressive baselines. Notably, on InterHuman, HINT achieves an FID of 3.100, significantly improving over the previous state-of-the-art score of 5.154.