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In-betweening is a technique for generating transitions given initial andtarget character states. The majority of existing works require multiple (often$>$10) frames as input, which are not always accessible. Our work deals with afocused yet challenging problem: to generate the transition when given exactlytwo frames (only the first and last). To cope with this challenging scenario,we implement our bi-directional scheme which generates forward and backwardtransitions from the start and end frames with two adversarial autoregressivenetworks, and stitches them in the middle of the transition where there is nostrict ground truth. The autoregressive networks based on conditionalvariational autoencoders (CVAE) are optimized by searching for a pair ofoptimal latent codes that minimize a novel stitching loss between theiroutputs. Results show that our method achieves higher motion quality and morediverse results than existing methods on both the LaFAN1 and Human3.6mdatasets.

Diverse Motion In-betweening with Dual Posture Stitching