@article{animajadaptiveinbetweeningsiggraph2026,
  title = {Adaptive Interpolation-Synthesis for Motion In-Betweening on Keyframe-Based Animation},
  author = {Anton Raël and Julien Boucher and Antoine Lhermitte},
  year = {2026},
  journal = {SIGGRAPH 2026 Conference Papers / arXiv},
  url = {https://arxiv.org/abs/2605.02742},
  abstract = {A SIGGRAPH 2026 conference paper from Animaj (Paris) describing the machine learning method behind the studio's production in-betweening tool. The Adaptive Interpolation-Synthesis (AIS) layer, combined with a bidirectional LSTM encoder, mirrors the animator's process by dynamically blending learned interpolation with direct pose synthesis, and a domain-based input keypose schedule aligns training with real production data. Integrated into Autodesk Maya, the studio reports in-betweening completed up to 3.5 times faster while preserving motion style. In-betweening is a foundational taught skill, and this is a rare production-proven, peer-reviewed account of restructuring that stage around machine learning while keeping the animator's blocking-to-polish workflow.},
  keywords = {machine-learning, character-animation, in-betweening, production-pipelines, studio-adoption},
  note = {AI \& Animation Education Knowledge Base}
}