2025/07/01 by Effendy, Edward, Kuan-Wei Tseng, Tseng, Kuan-Wei +2
Computer Science · Engineering · Neuroscience · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Human Pose and Action Recognition #Motor Control and Adaptation #Robot Manipulation and Learning
paper · pdf · doi:10.48550/arxiv.2507.00676
openalex publication_date 2025/07/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Accepted in the ICIP 2025 We present a novel transformer-based framework for whole-body grasping that addresses both pose generation and motion infilling, enabling realistic and stable object interactions. Our pipeline comprises three stages: Grasp Pose Generation for full-body grasp generation, Temporal Infilling for smooth motion continuity, and a LiftUp Transformer that refines downsampled joints back to high-resolution markers. To overcome the scarcity of hand-object interaction data, we introduce a data-efficient Generalized Pretraining stage on large, diverse motion datasets, yielding robust spatio-temporal representations transferable to grasping tasks. Experiments on the GRAB dataset show that our method outperforms state-of-the-art baselines in terms of coherence, stability, and visual realism. The modular design also supports easy adaptation to other human-motion applications.