FreeMotion: A Unified Framework for Number-free Text-to-Motion Synthesis
원문 초록
Text-to-motion synthesis is a crucial task in computer vision. Existing methods are limited in their universality, as they are tailored for single-person or two-person scenarios and can not be applied to generate motions for more individuals. To achieve the number-free motion synthesis, this paper reconsiders motion generation and proposes to unify the single and multi-person motion by the conditional motion distribution. Furthermore, a generation module and an interaction module are designed for our FreeMotion framework to decouple the process of conditional motion generation and finally support the number-free motion synthesis. Besides, based on our framework, the current single-person motion spatial control method could be seamlessly integrated, achieving precise control of multi-person motion. Extensive experiments demonstrate the superior performance of our method and our capability to infer single and multi-human motions simultaneously.
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AnyMoLe: Any Character Motion In-betweening Leveraging Video Diffusion Models
Motion Anything: Any to Motion Generation
Free-T2M: Frequency Enhanced Text-to-Motion Diffusion Model With Consistency Loss
KETA: Kinematic-Phrases-Enhanced Text-to-Motion Generation via Fine-grained Alignment
Sitcom-Crafter: A Plot-Driven Human Motion Generation System in 3D Scenes
BAD: Bidirectional Auto-regressive Diffusion for Text-to-Motion Generation
토론
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