This paper describes an alternative approach to motion synthesis based on the principle of motion nearest neighbors and motion matching.
GenMM is a generative model that can extract diverse motions from a single example sequence.
It can be used to perform tasks such as motion completion, key frame-guided generation, infinite looping, and motion reassembly.
The authors state that they have demonstrated impressive results using previous data-driven methods such as deep learning in motion synthesis.
However, they suffer from drawbacks that limit their practical applicability.
GenMM is an alternative approaches based upon the classical idea of motion near neighbors andmotion matching.
Motion synthesis methods are limited in their effectiveness.
Deep learning methods have been very effective at this task but they have a lot of drawbacks.
Gen MM is an addition to the list of motion synthesis methods discussed in this paper.
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