Text-conditioned 3D human--object interaction (HOI) generation remains challenging because individually plausible human and object motions do not necessarily form a coherent interaction. We argue that a key limitation lies in conventional skeleton-based representations, which compactly describe body kinematics while leaving surface-level interaction geometry largely implicit, often requiring additional contact-specific regularizers. We propose ULIFlow, a framework that explicitly models continuous human--object relations within a unified latent space. To this end, we derive continuous marker--object relations from sparse body-surface markers and treat them as an explicit interaction modality alongside human motion and object dynamics. A Mixture-of-Transformers (MoT) autoencoder is then designed to jointly model these heterogeneous streams while preserving their modality-specific structures. Importantly, we let the relation stream directly participate in human and object reconstruction, encouraging the shared latent representation to preserve both individual dynamics and the interaction geometry that coordinates them. To further achieve variable-length generation, we introduce a blockwise Flow Forcing scheme with global sequence-progress conditioning, enabling progressive continuation from fixed-length training windows while maintaining consistent long-horizon dynamics. Extensive experiments demonstrate that ULIFlow consistently outperforms prior work in overall generation quality and human--object coordination, while preserving coherent interactions over requested durations beyond the training horizon.

翻译：暂无翻译