@article{holdenmotionsynthesistestoftime2016,
  title = {A deep learning framework for character motion synthesis and editing (SIGGRAPH 2016 / SIGGRAPH 2026 Test-of-Time award)},
  author = {Holden, D. and Saito, J. and Komura, T.},
  year = {2016},
  journal = {ACM Digital Library},
  url = {https://dl.acm.org/doi/10.1145/2897824.2925975},
  abstract = {Published at SIGGRAPH 2016 and awarded the SIGGRAPH 2026 Test-of-Time award, this paper by Holden, Saito and Komura established the deep-learning approach to character motion synthesis and editing that has shaped the field for a decade. Using convolutional autoencoders, it enabled natural, controllable character motion generation from data rather than hand-keyed animation. The Test-of-Time award marks it as a canonical reference for animation curricula covering the origins and development of neural character animation.},
  keywords = {character-animation, motion-synthesis, generative-ai, production-practice},
  note = {AI \& Animation Education Knowledge Base}
}