2026-06-01原文2 min readUpdated: 2026-06-01

Dex2HOI: Dexterous Bimanual Two-Object Interaction Generation

Dex2HOI is a unified diffusion model for generating single- and two-object human-object interaction from text. It employs a Dual-Stream Diffusion approach with bidirectional cross-attention, a Motion Fusion Network with hand-relative object representations, and autoregressive sampling for real-time arbitrary-length sequence generation, achieving up to 540x speedup over prior methods.

SourcearXiv Computer VisionAuthor: Chrysa Pratikaki, Pablo Ruiz-Ponce, Jiankang Deng, Stefanos Zafeiriou, Rolandos Alexandros Potamias

[2605.30444] Dex2HOI: Dexterous Bimanual Two-Object Interaction Generation

[Submitted on 28 May 2026]

Title:Dex2HOI: Dexterous Bimanual Two-Object Interaction Generation

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Abstract:Recent advances in 4D Human-Object Interaction (HOI) generation have enabled increasingly realistic motion synthesis, particularly for single-object manipulation. Yet current research overlooks an inherent property of human behavior: people naturally coordinate both hands and manipulate multiple objects simultaneously. To address this gap, we present Dex2HOI, a unified diffusion model for single- and two-object HOI synthesis from text. At its core, Dex2HOI employs a Dual-Stream Diffusion approach, where each object is processed in a dedicated interaction stream and coordinated through bidirectional cross-attention. To synthesize the final motion, we introduce a Motion Fusion Network integrated with novel hand-relative object representations and contact-aware conditioning applied across the whole sequence. By sampling the diffusion process autoregressively over prefix-conditioned windows, Dex2HOI generates arbitrarily long sequences at real-time speed omitting redundant test-time optimization, achieving up to x540 inference speed-up over prior state-of-the-art methods. Extensive evaluation on both single- and two-object benchmarks demonstrates state-of-the-art quantitative results, marking a step beyond conventional single-object HOI generation and toward expressive multi-object manipulation. Code and models will be released upon acceptance.

Subjects:

Computer Vision and Pattern Recognition (cs.CV)

Cite as: arXiv:2605.30444 [cs.CV]

(or arXiv:2605.30444v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2605.30444

arXiv-issued DOI via DataCite

Submission history

From: Chrysa Pratikaki [view email] [v1] Thu, 28 May 2026 18:15:18 UTC (4,528 KB)

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