  We have recently seen tremendous progress in diffusion advances for
generating realistic human motions. Yet, they largely disregard the multi-human
interactions. In this paper, we present InterGen, an effective diffusion-based
approach that incorporates human-to-human interactions into the motion
diffusion process, which enables layman users to customize high-quality
two-person interaction motions, with only text guidance. We first contribute a
multimodal dataset, named InterHuman. It consists of about 107M frames for
diverse two-person interactions, with accurate skeletal motions and 23,337
natural language descriptions. For the algorithm side, we carefully tailor the
motion diffusion model to our two-person interaction setting. To handle the
symmetry of human identities during interactions, we propose two cooperative
transformer-based denoisers that explicitly share weights, with a mutual
attention mechanism to further connect the two denoising processes. Then, we
propose a novel representation for motion input in our interaction diffusion
model, which explicitly formulates the global relations between the two
performers in the world frame. We further introduce two novel regularization
terms to encode spatial relations, equipped with a corresponding damping scheme
during the training of our interaction diffusion model. Extensive experiments
validate the effectiveness and generalizability of InterGen. Notably, it can
generate more diverse and compelling two-person motions than previous methods
and enables various downstream applications for human interactions.
