Semantic-Driven Multi-character Multi-motion 3D Animation Generation

Semantic-driven animation generation significantly eases the workload of animators, but it still confronts a variety of challenges (e.g., natural language input, temporal reasoning under visualization, linking natural language with graphical systems, etc.). This paper tackles the issue of synchronized motion juxtaposition in terms of temporal visualization. We utilize semantic dependency analysis, prior probabilities and the Semantic Action Graph to extract and fuse synchronous motions, thus creating an advanced system for generating Semantic-driven 3D animations. The results demonstrate that our system proficiently generates natural and coherent 3D animations from text descriptions involving multiple characters and actions. This effectively overcomes the synchronized motion juxtaposition challenge and advances the field of Semantic-driven animation creation.