  Motion in-betweening, a fundamental task in character animation, consists of
generating motion sequences that plausibly interpolate user-provided keyframe
constraints. It has long been recognized as a labor-intensive and challenging
process. We investigate the potential of diffusion models in generating diverse
human motions guided by keyframes. Unlike previous inbetweening methods, we
propose a simple unified model capable of generating precise and diverse
motions that conform to a flexible range of user-specified spatial constraints,
as well as text conditioning. To this end, we propose Conditional Motion
Diffusion In-betweening (CondMDI) which allows for arbitrary dense-or-sparse
keyframe placement and partial keyframe constraints while generating
high-quality motions that are diverse and coherent with the given keyframes. We
evaluate the performance of CondMDI on the text-conditioned HumanML3D dataset
and demonstrate the versatility and efficacy of diffusion models for keyframe
in-betweening. We further explore the use of guidance and imputation-based
approaches for inference-time keyframing and compare CondMDI against these
methods.
