SurgCast: 手術世界モデルを制御するための行動条件付き未来骨格予測
SurgCast: Action-Conditioned Future Skeletons for Controllable Surgical World Models
現在の構造・ロボット状態・将来の行動から手術器具の未来骨格を予測し、それを用いて手術動画生成を制御する手法を提案。
著者: Wanhao Liu, Rulin Zhou, Liangjing Shao, Zhaocheng Lin, Dongyue Li, Jinsong Lin, Guoheng Ma, Hangheng Yang, Zhiqing Tang, Jing Chen, Panshuo Li, Hongliang Ren
分類: cs.CV
原文アブストラクト
Surgical world models must turn prospective robot actions into visually faithful, structurally consistent instrument motion. We introduce SurgCast, which predicts future instrument skeletons from current structure, robot state, and future actions, then uses the forecast to control video generation. Dual-path injection combines skeleton geometry with derived, instrument-specific motion descriptors through geometric residuals and conditioning modulation. Both paths complement native action conditioning and are retained in a four-step causal student. On SutureBot and SRTH-Porcine-Cholei, the teacher and student improve all seven reported quality and instrument-consistency metrics over their respective direct-action baselines; teacher FVD decreases by 37.6% and 67.3%. Ablations show that future actions improve skeleton prediction and that combining both injection paths performs best across all six metrics under predicted skeletons. These results support using predicted articulated structure and its motion semantics as a control interface for surgical video prediction. Project videos and examples are available at https://surgcast.netlify.app/.