  In this work, we propose TextIM, a novel framework for synthesizing
TEXT-driven human Interactive Motions, with a focus on the precise alignment of
part-level semantics. Existing methods often overlook the critical roles of
interactive body parts and fail to adequately capture and align part-level
semantics, resulting in inaccuracies and even erroneous movement outcomes. To
address these issues, TextIM utilizes a decoupled conditional diffusion
framework to enhance the detailed alignment between interactive movements and
corresponding semantic intents from textual descriptions. Our approach
leverages large language models, functioning as a human brain, to identify
interacting human body parts and to comprehend interaction semantics to
generate complicated and subtle interactive motion. Guided by the refined
movements of the interacting parts, TextIM further extends these movements into
a coherent whole-body motion. We design a spatial coherence module to
complement the entire body movements while maintaining consistency and harmony
across body parts using a part graph convolutional network. For training and
evaluation, we carefully selected and re-labeled interactive motions from
HUMANML3D to develop a specialized dataset. Experimental results demonstrate
that TextIM produces semantically accurate human interactive motions,
significantly enhancing the realism and applicability of synthesized
interactive motions in diverse scenarios, even including interactions with
deformable and dynamically changing objects.
