# MFlowHOI: Whole-Body Human-Object Interaction Generation via Part-Aware MeanFlow

arXiv: https://arxiv.org/abs/2610.16252 · HTML: https://mlsanity.com/paper/2610.16252/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 29 of its 37 references that are in the map.
- First posted on arXiv 12 Oct 2026.

## Builds on (most cited first, 29 of 29)

- [Autoregressive Image Generation using Residual Quantization](https://mlsanity.com/paper/2203.01941/) (2022)
- [Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation](https://mlsanity.com/paper/2311.17117/) (2023)
- [Mean Flows for One-step Generative Modeling](https://mlsanity.com/paper/2505.13447/) (2025)
- [GRAB: A Dataset of Whole-Body Human Grasping of Objects](https://mlsanity.com/paper/2008.11200/) (2020)
- [OmniControl: Control Any Joint at Any Time for Human Motion Generation](https://mlsanity.com/paper/2310.08580/) (2023)
- [Object Motion Guided Human Motion Synthesis](https://mlsanity.com/paper/2309.16237/) (2023)
- [GOAL: Generating 4D Whole-Body Motion for Hand-Object Grasping](https://mlsanity.com/paper/2112.11454/) (2021)
- [IMos: Intent-Driven Full-Body Motion Synthesis for Human-Object Interactions](https://mlsanity.com/paper/2212.07555/) (2022)
- [D-Grasp: Physically Plausible Dynamic Grasp Synthesis for Hand-Object Interactions](https://mlsanity.com/paper/2112.03028/) (2021)
- [TLControl: Trajectory and Language Control for Human Motion Synthesis](https://mlsanity.com/paper/2311.17135/) (2023)
- [SAGA: Stochastic Whole-Body Grasping with Contact](https://mlsanity.com/paper/2112.10103/) (2021)
- [Text2HOI: Text-guided 3D Motion Generation for Hand-Object Interaction](https://mlsanity.com/paper/2404.00562/) (2024)
- [AvatarReX: Real-time Expressive Full-body Avatars](https://mlsanity.com/paper/2305.04789/) (2023)
- [TOCH: Spatio-Temporal Object-to-Hand Correspondence for Motion Refinement](https://mlsanity.com/paper/2205.07982/) (2022)
- [Human-Object Interaction from Human-Level Instructions](https://mlsanity.com/paper/2406.17840/) (2024)
- [DiffH2O: Diffusion-Based Synthesis of Hand-Object Interactions from Textual Descriptions](https://mlsanity.com/paper/2403.17827/) (2024)
- [CAMS: CAnonicalized Manipulation Spaces for Category-Level Functional Hand-Object Manipulation Synthesis](https://mlsanity.com/paper/2303.15469/) (2023)
- [ArtiGrasp: Physically Plausible Synthesis of Bi-Manual Dexterous Grasping and Articulation](https://mlsanity.com/paper/2309.03891/) (2023)
- [ContactGen: Generative Contact Modeling for Grasp Generation](https://mlsanity.com/paper/2310.03740/) (2023)
- [Motion Flow Matching for Human Motion Synthesis and Editing](https://mlsanity.com/paper/2312.08895/) (2023)
- [GeneOH Diffusion: Towards Generalizable Hand-Object Interaction Denoising via Denoising Diffusion](https://mlsanity.com/paper/2402.14810/) (2024)
- [HOIGPT: Learning Long Sequence Hand-Object Interaction with Language Models](https://mlsanity.com/paper/2503.19157/) (2025)
- [GRIP: Generating Interaction Poses Using Spatial Cues and Latent Consistency](https://mlsanity.com/paper/2308.11617/) (2023)
- [HOIDiNi: Human-Object Interaction through Diffusion Noise Optimization](https://mlsanity.com/paper/2506.15625/) (2025)
- [CoDA: Coordinated Diffusion Noise Optimization for Whole-Body Manipulation of Articulated Objects](https://mlsanity.com/paper/2505.21437/) (2025)
- [FlowerDance: MeanFlow for Efficient and Refined 3D Dance Generation](https://mlsanity.com/paper/2511.21029/) (2025)
- [GraspDiffusion: Synthesizing Realistic Whole-body Hand-Object Interaction](https://mlsanity.com/paper/2410.13911/) (2024)
- [FlowMotion: Target-Predictive Conditional Flow Matching for Jitter-Reduced Text-Driven Human Motion Generation](https://mlsanity.com/paper/2504.01338/) (2025)
- [ARMFlow: AutoRegressive MeanFlow for Online 3D Human Reaction Generation](https://mlsanity.com/paper/2512.16234/) (2025)

## Abstract (arXiv)

Whole-body human-object interaction (HOI) generation requires coordinating body and finger motion, object trajectories, and time-varying contacts. Existing methods typically rely on iterative diffusion or test-time optimization, resulting in high inference costs. Direct one-step generation is highly desired in downstream applications due to efficient inference. However, it is challenging because these HOI components differ substantially in structure and scale. We present MFlowHOI, an efficient one-step framework for whole-body HOI generation. A Contact-Aware Multipart RVQVAE organizes body, hand, object, and contact signals into a structured latent space, with a shared decoder preserving their global coordination. An ST-DiT then models temporal and cross-part dependencies in this space and generates a complete interaction in a single MeanFlow evaluation. Predicted object-local contact correspondences further enable geometric refinement and interaction-preserving transfer to photorealistic 3D Gaussian avatars with different body proportions. Experiments demonstrate that MFlowHOI outperforms existing base generators in motion quality and text alignment while substantially reducing inference time.