TY  - JOUR
TI  - A deep learning framework for character motion synthesis and editing (SIGGRAPH 2016 / SIGGRAPH 2026 Test-of-Time award)
AU  - Holden, D.
AU  - Saito, J.
AU  - Komura, T.
PY  - 2016
DA  - 2016/07//
JO  - ACM Digital Library
AB  - 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.
KW  - character-animation
KW  - motion-synthesis
KW  - generative-ai
KW  - production-practice
UR  - https://dl.acm.org/doi/10.1145/2897824.2925975
LA  - en
ER  -