OneHOI: Unifying Human-Object Interaction Generation and Editing

TL;DR

OneHOI is a unified diffusion transformer framework that advances human-object interaction generation and editing by modeling relations and disentangling multiple interactions, achieving state-of-the-art results.

Contribution

It introduces a single conditional denoising process that combines HOI generation and editing with shared structured representations and novel attention mechanisms.

Findings

Achieves state-of-the-art results in HOI generation and editing.

Supports diverse control conditions including layout-guided and arbitrary masks.

Effectively models multi-HOI scenes with disentangled representations.

Abstract

Human-Object Interaction (HOI) modelling captures how humans act upon and relate to objects, typically expressed as <person, action, object> triplets. Existing approaches split into two disjoint families: HOI generation synthesises scenes from structured triplets and layout, but fails to integrate mixed conditions like HOI and object-only entities; and HOI editing modifies interactions via text, yet struggles to decouple pose from physical contact and scale to multiple interactions. We introduce OneHOI, a unified diffusion transformer framework that consolidates HOI generation and editing into a single conditional denoising process driven by shared structured interaction representations. At its core, the Relational Diffusion Transformer (R-DiT) models verb-mediated relations through role- and instance-aware HOI tokens, layout-based spatial Action Grounding, a Structured HOI Attention to enforce interaction topology, and HOI RoPE to disentangle multi-HOI scenes. Trained jointly with modality dropout on our HOI-Edit-44K, along with HOI and object-centric datasets, OneHOI supports layout-guided, layout-free, arbitrary-mask, and mixed-condition control, achieving state-of-the-art results across both HOI generation and editing. Code is available at https://jiuntian.github.io/OneHOI/.