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

• URL: http://arxiv.org/abs/2506.09075v1
• Date: Mon, 09 Jun 2025 19:26:27 GMT
• Title: SILK: Smooth InterpoLation frameworK for motion in-betweening A Simplified Computational Approach
• Authors: Elly Akhoundi, Hung Yu Ling, Anup Anand Deshmukh, Judith Butepage,
• Abstract summary: Motion in-betweening is a crucial tool for animators, enabling control over pose-level details in each pose.<n>Recent machine learning solutions for motion in-betweening rely on complex models, skeleton-aware architectures or requiring multiple modules and training steps.<n>We introduce a simple yet effective Transformer-based framework, employing a single encoder to synthesize realistic motions for motion in-betweening tasks.
• Score: 1.7812314225208412
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Motion in-betweening is a crucial tool for animators, enabling intricate control over pose-level details in each keyframe. Recent machine learning solutions for motion in-betweening rely on complex models, incorporating skeleton-aware architectures or requiring multiple modules and training steps. In this work, we introduce a simple yet effective Transformer-based framework, employing a single Transformer encoder to synthesize realistic motions for motion in-betweening tasks. We find that data modeling choices play a significant role in improving in-betweening performance. Among others, we show that increasing data volume can yield equivalent or improved motion transitions, that the choice of pose representation is vital for achieving high-quality results, and that incorporating velocity input features enhances animation performance. These findings challenge the assumption that model complexity is the primary determinant of animation quality and provide insights into a more data-centric approach to motion interpolation. Additional videos and supplementary material are available at https://silk-paper.github.io.

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