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기록

MOTION GENERATION MODEL-BASED MOTION GENERATION METHOD AND APPARATUS, AND DEVICE

발명심사 중
1조회수
20청구항 · 3 독립항
§ Ⅰ

개요

발명자

Yang WU; Zhenyu XIE; Zhongqian SUN; Wei YANG; Xiaodan LIANG

IPC 분류

G6T 13/40G6T 5/60G6T 5/70

CPC 분류

G6T13/40G6T5/60G6T5/70

Motion generation model-based motion generation method, device, and storage medium relate to the field of artificial intelligence technologies. The method includes: obtaining a text containing motion information; generating a text feature of the text through a text encoder; generating an intermediate motion sequence in a feature space of a first dimension based on the text feature through a first diffusion model; and performing detail enhancement processing on the intermediate motion sequence in a feature space of a second dimension through a second diffusion model, to obtain an output motion sequence matching the text, the second dimension being greater than the first dimension. In this application, the intermediate motion sequence is preliminarily generated through the first diffusion model, and detail enhancement processing is performed on the intermediate motion sequence through the second diffusion model, thereby improving the richness of details in the output motion sequence.

원문 (중국어)

Motion generation model-based motion generation method, device, and storage medium relate to the field of artificial intelligence technologies. The method includes: obtaining a text containing motion information; generating a text feature of the text through a text encoder; generating an intermediate motion sequence in a feature space of a first dimension based on the text feature through a first diffusion model; and performing detail enhancement processing on the intermediate motion sequence in a feature space of a second dimension through a second diffusion model, to obtain an output motion sequence matching the text, the second dimension being greater than the first dimension. In this application, the intermediate motion sequence is preliminarily generated through the first diffusion model, and detail enhancement processing is performed on the intermediate motion sequence through the second diffusion model, thereby improving the richness of details in the output motion sequence.