Diverse Motion In-Betweening From Sparse Keyframes With Dual Posture Stitching

In-betweening is a technique for generating transitions given start and target character states. The majority of existing works require multiple (often $\geq$ 10) frames as input, which are not always available. In addition, they produce results that lack diversity, which may not fulfill artists’ requirements. Addressing these gaps, our work deals with a focused yet challenging problem: generating diverse and high-quality transitions given exactly two frames (only the start and target frames). To cope with this challenging scenario, we propose a bi-directional motion generation and stitching scheme which generates forward and backward transitions from the start and target frames with two adversarial autoregressive networks, respectively, and stitches them midway between the start and target frames. In contrast to stitching at the start or target frames, where the ground truth cannot be altered, there is no strict midway ground truth. Thus, our method can capitalize on this flexibility and generate high-quality and diverse transitions simultaneously. Specifically, we employ conditional variational autoencoders (CVAEs) to implement our autoregressive networks and propose a novel stitching loss to stitch the bi-directional generated motions around the midway point. Extensive experiments demonstrate that our method achieves higher motion quality and more diverse results than existing methods on the LaFAN1, Human3.6m and AMASS datasets.