用自回归扩散模型生成连贯的3D人物交互动作，更自然且更快。
Auto-Regressive Diffusion for Generating 3D Human-Object Interactions
- 通过对比变分自编码器学习物理合理的连续动作空间。
- 在OMOMO和BEHAVE数据集上优于现有方法，推理速度更快。
- 适合动画、游戏、VR等需要长序列自然交互的场景。
文本驱动的人-物交互（Text-to-HOI）生成是动画、视频游戏、虚拟现实和机器人领域的新兴方向。长序列中保持交互一致性是主要挑战。现有基于文本到动作的方法（如离散动作标记化）难以直接应用于该领域，因数据有限且模态复杂。为此，我们提出自回归扩散模型（ARDHOI），通过预测连续动作标记实现生成。引入对比变分自编码器（cVAE）学习物理合理的连续人-物交互标记空间，确保生成动作自然真实。采用Mamba-based上下文编码器捕捉并维持序列一致性，并使用MLP去噪器基于编码上下文生成后续标记。模型在OMOMO和BEHAVE数据集上评估，性能与推理速度均优于当前最优方法，为文本驱动的人-物交互任务提供高效可靠解决方案。
原文摘要 · Abstract (English)
Text-driven Human-Object Interaction (Text-to-HOI) generation is an emerging field with applications in animation, video games, virtual reality, and robotics. A key challenge in HOI generation is maintaining interaction consistency in long sequences. Existing Text-to-Motion-based approaches, such as discrete motion tokenization, cannot be directly applied to HOI generation due to limited data in this domain and the complexity of the modality. To address the problem of interaction consistency in long sequences, we propose an autoregressive diffusion model (ARDHOI) that predicts the next continuous token. Specifically, we introduce a Contrastive Variational Autoencoder (cVAE) to learn a physically plausible space of continuous HOI tokens, thereby ensuring that generated human-object motions are realistic and natural. For generating sequences autoregressively, we develop a Mamba-based context encoder to capture and maintain consistent sequential actions. Additionally, we implement an MLP-based denoiser to generate the subsequent token conditioned on the encoded context. Our model has been evaluated on the OMOMO and BEHAVE datasets, where it outperforms existing state-of-the-art methods in terms of both performance and inference speed. This makes ARDHOI a robust and efficient solution for text-driven HOI tasks
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。