Conditional human motion synthesis (HMS) aims to generate human motion
sequences that conform to specific conditions. Text and audio represent the two
predominant modalities employed as HMS control conditions. While existing
research has primarily focused on single conditions, the multi-condition human
motion synthesis remains underexplored. In this study, we propose a
multi-condition HMS framework, termed MCM, based on a dual-branch structure
composed of a main branch and a control branch. This framework effectively
extends the applicability of the diffusion model, which is initially predicated
solely on textual conditions, to auditory conditions. This extension
encompasses both music-to-dance and co-speech HMS while preserving the
intrinsic quality of motion and the capabilities for semantic association
inherent in the original model. Furthermore, we propose the implementation of a
Transformer-based diffusion model, designated as MWNet, as the main branch.
This model adeptly apprehends the spatial intricacies and inter-joint
correlations inherent in motion sequences, facilitated by the integration of
multi-wise self-attention modules. Extensive experiments show that our method
achieves competitive results in single-condition and multi-condition HMS tasks.

提出了基于双分支结构的多条件人体运动合成（MCM）框架，其中主分支采用基于 Transformer 的扩散模型（MWNet），有效扩展了扩散模型的适用性，同时保持运动的固有质量和语义关联能力。在单条件和多条件人体运动合成任务中实现了有竞争力的结果。