BUMP: Body-driven Unified Multi-object Physics for HOI Synthesis
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
arXiv:2609.38466v1 Announce Type: new Abstract: Text-conditioned full-body human-object interaction (HOI) generation requires synthesizing human motion and object trajectories that match the input te...
Synthesizing realistic Human-Object Interactions (HOI) is critical for creating embodied avatars and functional virtual environments. However, current data-driven approaches primarily rely on motion capture datasets, which are expensive to scale and limited in functional diversity.
Uni-HOI is a unified framework that learns the joint distribution among text, human motion, and object motion for 4D human‑object interaction (HOI). It uses large language models and two motion‑specific VQ‑VAEs to convert heterogeneous motion data into token sequences, enabling seamless integration of all three modalities. A two‑stage training strategy first captures correlations on a large‑scale HOI dataset and then fine‑tunes for specific tasks, achieving strong performance on text‑driven HOI generation, object‑motion‑driven human motion generation, and human‑motion‑driven object motion prediction.
MAMHOI introduces an affordance-mediated factorization for generating scene-aware human‑object interactions. It separates scene understanding and motion synthesis: a scene‑conditioned model predicts feasible interaction locations and modes, while an affordance‑conditioned model generates the corresponding human‑object motion. This approach enables learning from separate human‑scene and human‑object datasets, reducing object‑scene penetration and improving interaction realism in complex indoor environments.
Long-horizon human-object-scene interaction (HOSI) is central to embodied AI, animation, and game development. Existing human-object interaction (HOI) and human-scene interaction (HSI) models excel in...
SMART introduces a scalable system for zero‑shot sim‑to‑real articulated object manipulation by leveraging large‑scale synthetic pretraining. The core component, SMART‑Sim, is an articulation‑aware simulation platform that facilitates efficient task generation and demonstration collection. Using SMART‑Sim, the authors synthesize SMART‑Data, comprising over 1 million demonstrations across 44 task types, 5 robot setups, and 2,507 articulated objects, and demonstrate that a vision‑language‑action model pretrained on this data achieves competitive simulation performance and zero‑shot transfer to real‑world tasks.