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Current training of motion style transfer systems relies on consistencylosses across style domains to preserve contents, hindering its scalableapplication to a large number of domains and private data. Recent imagetransfer works show the potential of independent training on each domain byleveraging implicit bridging between diffusion models, with the contentpreservation, however, limited to simple data patterns. We address this byimposing biased sampling in backward diffusion while maintaining the domainindependence in the training stage. We construct the bias from the sourcedomain keyframes and apply them as the gradient of content constraints,yielding a framework with keyframe manifold constraint gradients (KMCGs). Ourvalidation demonstrates the success of training separate models to transferbetween as many as ten dance motion styles. Comprehensive experiments find asignificant improvement in preserving motion contents in comparison to baselineand ablative diffusion-based style transfer models. In addition, we perform ahuman study for a subjective assessment of the quality of generated dancemotions. The results validate the competitiveness of KMCGs.

Scalable Motion Style Transfer with Constrained Diffusion Generation