# InterControl: Zero-shot Human Interaction Generation by Controlling Every Joint

arXiv: https://arxiv.org/abs/2311.15864 · HTML: https://mlsanity.com/paper/2311.15864/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Placed in Video generation, one of 14 directions in Diffusion models.
- Builds on 59 of its 74 references that are in the map.
- First posted on arXiv 27 Nov 2023.

## Placed in

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

## Builds on (most cited first, 30 of 59)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [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)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Imagen Video: High Definition Video Generation with Diffusion Models](https://mlsanity.com/paper/2210.02303/) (2022)
- [Expressive Body Capture: 3D Hands, Face, and Body from a Single Image](https://mlsanity.com/paper/1904.05866/) (2019)
- [AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning](https://mlsanity.com/paper/2307.04725/) (2023)
- [DiffWave: A Versatile Diffusion Model for Audio Synthesis](https://mlsanity.com/paper/2009.09761/) (2020)
- [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)
- [ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2108.02938/) (2021)
- [Structure and Content-Guided Video Synthesis with Diffusion Models](https://mlsanity.com/paper/2302.03011/) (2023)
- [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)
- [Improving Diffusion Models for Inverse Problems using Manifold Constraints](https://mlsanity.com/paper/2206.00941/) (2022)
- [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)
- [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)

## Built on by (most cited first, 23 of 23)

- [LLMs Meet Multimodal Generation and Editing: A Survey](https://mlsanity.com/paper/2405.19334/) (2024)
- [A Survey on Human Interaction Motion Generation](https://mlsanity.com/paper/2503.12763/) (2025)
- [Human Motion Video Generation: A Survey](https://mlsanity.com/paper/2509.03883/) (2025)
- [DuetGen: Music Driven Two-Person Dance Generation via Hierarchical Masked Modeling](https://mlsanity.com/paper/2506.18680/) (2025)
- [PINO: Person-Interaction Noise Optimization for Long-Duration and Customizable Motion Generation of Arbitrary-Sized Groups](https://mlsanity.com/paper/2507.19292/) (2025)
- [Multi-Person Interaction Generation from Two-Person Motion Priors](https://mlsanity.com/paper/2505.17860/) (2025)
- [Interact2Ar: Full-Body Human-Human Interaction Generation via Autoregressive Diffusion Models](https://mlsanity.com/paper/2512.19692/) (2025)
- [InterAgent: Physics-based Multi-agent Command Execution via Diffusion on Interaction Graphs](https://mlsanity.com/paper/2512.07410/) (2025)
- [FineXtrol: Controllable Motion Generation via Fine-Grained Text](https://mlsanity.com/paper/2511.18927/) (2025)
- [HINT: Hierarchical Interaction Modeling for Autoregressive Multi-Human Motion Generation](https://mlsanity.com/paper/2601.20383/) (2026)
- [Bridging Semantic and Kinematic Conditions with Diffusion-based Discrete Motion Tokenizer](https://mlsanity.com/paper/2603.19227/) (2026)
- [Disentangled Hierarchical VAE for 3D Human-Human Interaction Generation](https://mlsanity.com/paper/2603.00144/) (2026)
- [MoReact: Generating Reactive Motion from Textual Descriptions](https://mlsanity.com/paper/2509.23911/) (2025)
- [Dex2HOI: Dexterous Bimanual Two-Object Interaction Generation](https://mlsanity.com/paper/2605.30444/) (2026)
- [DyaPlex: Full-Duplex Speech-Motion Model for Dyadic Interaction](https://mlsanity.com/paper/2606.03874/) (2026)
- [ReactDance: Hierarchical Representation for High-Fidelity and Coherent Long-Form Reactive Dance Generation](https://mlsanity.com/paper/2505.05589/) (2025)
- [ReactMotion: Generating Reactive Listener Motions from Speaker Utterance](https://mlsanity.com/paper/2603.15083/) (2026)
- [ReMoGen: Real-time Human Interaction-to-Reaction Generation via Modular Learning from Diverse Data](https://mlsanity.com/paper/2604.01082/) (2026)
- [Stability-Driven Motion Generation for Object-Guided Human-Human Co-Manipulation](https://mlsanity.com/paper/2604.20336/) (2026)
- [KV-Control: Parameter-Efficient K/V Injection for Trajectory-Controlled Text-to-Motion](https://mlsanity.com/paper/2606.05624/) (2026)
- [InterEdit: Navigating Text-Guided 3D Dyadic Human Motion Editing](https://mlsanity.com/paper/2603.13082/) (2026)
- [Paper Reconstruction Evaluation: Evaluating Presentation and Hallucination in AI-written Papers](https://mlsanity.com/paper/2604.01128/) (2026)
- [COSMI: COmpositional Synthesis of Multi-object Interactions](https://mlsanity.com/paper/2610.03252/) (2026)

## Abstract (arXiv)

Text-conditioned motion synthesis has made remarkable progress with the emergence of diffusion models. However, the majority of these motion diffusion models are primarily designed for a single character and overlook multi-human interactions. In our approach, we strive to explore this problem by synthesizing human motion with interactions for a group of characters of any size in a zero-shot manner. The key aspect of our approach is the adaptation of human-wise interactions as pairs of human joints that can be either in contact or separated by a desired distance. In contrast to existing methods that necessitate training motion generation models on multi-human motion datasets with a fixed number of characters, our approach inherently possesses the flexibility to model human interactions involving an arbitrary number of individuals, thereby transcending the limitations imposed by the training data. We introduce a novel controllable motion generation method, InterControl, to encourage the synthesized motions maintaining the desired distance between joint pairs. It consists of a motion controller and an inverse kinematics guidance module that realistically and accurately aligns the joints of synthesized characters to the desired location. Furthermore, we demonstrate that the distance between joint pairs for human-wise interactions can be generated using an off-the-shelf Large Language Model (LLM). Experimental results highlight the capability of our framework to generate interactions with multiple human characters and its potential to work with off-the-shelf physics-based character simulators. Code is available at https://github.com/zhenzhiwang/intercontrol