in2IN: Leveraging Individual Information to Generate Human INteractions

Pablo Ruiz-Ponce, German Barquero, Cristina Palmero, Sergio Escalera, José García-Rodríguez; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 1941-1951

Generating human-human motion interactions conditioned on textual descriptions is a very useful application in many areas such as robotics gaming animation and the metaverse. Alongside this utility also comes a great difficulty in modeling the highly dimensional inter-personal dynamics. In addition properly capturing the intra-personal diversity of interactions has a lot of challenges. Current methods generate interactions with limited diversity of intra-person dynamics due to the limitations of the available datasets and conditioning strategies. For this we introduce in2IN a novel diffusion model for human-human motion generation which is conditioned not only on the textual description of the overall interaction but also on the individual descriptions of the actions performed by each person involved in the interaction. To train this model we use a large language model to extend the InterHuman dataset with individual descriptions. As a result in2IN achieves state-of-the-art performance in the InterHuman dataset. Furthermore in order to increase the intra-personal diversity on the existing interaction datasets we propose DualMDM a model composition technique that combines the motions generated with in2IN and the motions generated by a single-person motion prior pre-trained on HumanML3D. As a result DualMDM generates motions with higher individual diversity and improves control over the intra-person dynamics while maintaining inter-personal coherence.