Reinforcement learning to train a character using disparate target animation data
Номер патента: EP3939010A1
Опубликовано: 19-01-2022
Автор(ы): Michael Taylor
Принадлежит: Sony Interactive Entertainment Inc
Опубликовано: 19-01-2022
Автор(ы): Michael Taylor
Принадлежит: Sony Interactive Entertainment Inc
Реферат: A method for training an animation character, including mapping first animation data defining a first motion sequence to a first subset of bones of a trained character, and mapping second animation data defining a second motion sequence to a second subset of bones. A bone hierarchy includes the first subset of bones and second subset of bones. Reinforcement learning is applied iteratively for training the first subset of bones using the first animation data and for training the second subset of bones using the second animation data. Training of each subset of bones is performed concurrently at each iteration. Training includes adjusting orientations of bones. The first subset of bones is composited with the second subset of bones at each iteration by applying physics parameters of a simulation environment to the adjusted orientations of bones in the first and second subset of bones.
Reinforcement learning to train a character using disparate target animation data
Номер патента: WO2020190415A1. Автор: Michael Taylor. Владелец: Sony Interactive Entertainment Inc.. Дата публикации: 2020-09-24.