TY - ABST
T1 - Lightweight Quaternion Transition Generation with Neural Networks
AU - Geleijn, Romi
AU - Radziszewski, Adrian
AU - Beryl van Straaten, Julia
AU - Debarba, Henrique Galvan
PY - 2021
Y1 - 2021
N2 - This paper introduces the Quaternion Transition Generator (QTG), a new network architecture tailored to animation transition generation for virtual characters. The QTG is simpler than the current state of the art, making it lightweight and easier to implement. It uses approximately 80% fewer arithmetic operations compared to other transition networks. Additionally, this architecture is capable of generating visually accurate rotation-based animations transitions and results in a lower Mean Absolute Error than transition generation techniques that are commonly used for animation blending.
AB - This paper introduces the Quaternion Transition Generator (QTG), a new network architecture tailored to animation transition generation for virtual characters. The QTG is simpler than the current state of the art, making it lightweight and easier to implement. It uses approximately 80% fewer arithmetic operations compared to other transition networks. Additionally, this architecture is capable of generating visually accurate rotation-based animations transitions and results in a lower Mean Absolute Error than transition generation techniques that are commonly used for animation blending.
KW - Quaternion Transition Generator
KW - Animation transition generation
KW - Network architecture
KW - Virtual characters
KW - Rotation-based animations
KW - Quaternion Transition Generator
KW - Animation transition generation
KW - Network architecture
KW - Virtual characters
KW - Rotation-based animations
U2 - 10.1109/VRW52623.2021.00172
DO - 10.1109/VRW52623.2021.00172
M3 - Conference abstract in proceedings
BT - 2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)
PB - IEEE
ER -