用扩散模型实现双手协同多物体交互动作生成，速度快效果好。
Dex2HOI: Dexterous Bimanual Two-Object Interaction Generation
- 双流扩散架构分别处理两物体，通过双向交叉注意力协调双手动作。
- 实测推理速度比之前方法快540倍，支持任意长序列实时生成。
- 适合需要真实双手多物操作的虚拟仿真、机器人训练场景。
近期4D人-物交互（HOI）生成进展推动了单物体操作的逼真运动合成，但忽视了人类行为中自然存在的双手协同与多物体并行操作特性。为此，我们提出Dex2HOI，一种统一的扩散模型，可从文本生成单物体与双物体交互动作。核心采用双流扩散机制，每个物体在独立交互流中处理，并通过双向交叉注意力实现协调。为生成最终动作，引入融合网络，结合新颖的手-物相对表示与全序列接触感知条件。通过前缀条件下的自回归采样，Dex2HOI在无需冗余测试时优化的情况下实现任意长度序列的实时生成，推理速度相比先前最优方法提升达540倍。在单/双物体基准上广泛评估显示其达到最先进定量结果，标志着从传统单物体生成迈向更具表现力的多物体操作。代码与模型将在论文接收后发布。
原文摘要 · Abstract (English)
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.
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