  Existing 3D human object interaction (HOI) datasets and models simply align
global descriptions with the long HOI sequence, while lacking a detailed
understanding of intermediate states and the transitions between states. In
this paper, we argue that fine-grained semantic alignment, which utilizes
state-level descriptions, offers a promising paradigm for learning semantically
rich HOI representations. To achieve this, we introduce Semantic-HOI, a new
dataset comprising over 20K paired HOI states with fine-grained descriptions
for each HOI state and the body movements that happen between two consecutive
states. Leveraging the proposed dataset, we design three state-level HOI tasks
to accomplish fine-grained semantic alignment within the HOI sequence.
Additionally, we propose a unified model called F-HOI, designed to leverage
multimodal instructions and empower the Multi-modal Large Language Model to
efficiently handle diverse HOI tasks. F-HOI offers multiple advantages: (1) It
employs a unified task formulation that supports the use of versatile
multimodal inputs. (2) It maintains consistency in HOI across 2D, 3D, and
linguistic spaces. (3) It utilizes fine-grained textual supervision for direct
optimization, avoiding intricate modeling of HOI states. Extensive experiments
reveal that F-HOI effectively aligns HOI states with fine-grained semantic
descriptions, adeptly tackling understanding, reasoning, generation, and
reconstruction tasks.
