Auto-Regressive Diffusion for Generating 3D Human-Object Interactions
This work addresses a key problem in animation, gaming, VR, and robotics by improving long-sequence consistency in HOI generation, though it is incremental as it builds on existing diffusion and autoregressive methods.
The paper tackles the challenge of maintaining interaction consistency in long sequences for text-driven human-object interaction generation by proposing an autoregressive diffusion model (ARDHOI) that predicts continuous tokens, achieving state-of-the-art performance and inference speed on OMOMO and BEHAVE datasets.
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