Conditional Motion In-betweening
원문 초록
Motion in-betweening (MIB) is a process of generating intermediate skeletal movement between the given start and target poses while preserving the naturalness of the motion, such as periodic footstep motion while walking. Although state-of-the-art MIB methods are capable of producing plausible motions given sparse key-poses, they often lack the controllability to generate motions satisfying the semantic contexts required in practical applications. We focus on the method that can handle pose or semantic conditioned MIB tasks using a unified model. We also present a motion augmentation method to improve the quality of pose-conditioned motion generation via defining a distribution over smooth trajectories. Our proposed method outperforms the existing state-of-the-art MIB method in pose prediction errors while providing additional controllability.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
AnyMoLe: Any Character Motion In-betweening Leveraging Video Diffusion Models
Versatile Physics-based Character Control with Hybrid Latent Representation
Diffuse-CLoC: Guided Diffusion for Physics-based Character Look-ahead Control
SViMo: Synchronized Diffusion for Video and Motion Generation in Hand-object Interaction Scenarios
EPFL-Smart-Kitchen-30: Densely annotated cooking dataset with 3D kinematics to challenge video and language models
MMGT: Motion Mask Guided Two-Stage Network for Co-Speech Gesture Video Generation
토론
이 논문과 연결된 포럼 글이 아직 없습니다.