<ul data-eligibleForWebStory="true"><li>Motion in-betweening, used by animators for detailed control, is typically facilitated by complex machine learning models.</li><li>A new study introduces a simple Transformer-based framework for motion in-betweening, using a single Transformer encoder.</li><li>The research emphasizes the role of data modeling choices in enhancing in-betweening performance.</li><li>Increasing data volume can lead to improved motion transitions.</li><li>The choice of pose representation significantly influences result quality in motion synthesis.</li><li>Incorporating velocity input features is highlighted as beneficial for animation performance.</li><li>The study challenges the idea that model complexity is the main factor for animation quality.</li><li>Insights from the research advocate for a more data-centric approach to motion interpolation.</li><li>Additional videos and supplementary material can be accessed at https://silk-paper.github.io.</li></ul>

SILK: Smooth InterpoLation frameworK for motion in-betweening A Simplified Computational Approach

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