RoGSW4RLD: ロボット世界モデルのロールアウトのためのフィードフォワード4Dガウシアンリフティング
RoGSW4RLD: Feed-Forward 4D Gaussian Lifting for Robot World Model Rollouts
複数カメラの映像予測を、視点と時間をまたいで問い合わせ可能な統一的な4Dガウシアン場に変換するフィードフォワード手法を提案し、新規視点の画質・深度・ロボット変位精度を大幅に改善した。
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著者: Jin Hyun Kim, Min Young Kim, Soohwan Song, Daekyum Kim
分類: cs.CV, cs.RO
原文アブストラクト
Action-conditioned video world models predict future robot interactions from multiple cameras, yet their outputs remain disparate video collections rather than a shared metric scene queryable across viewpoints and time. While existing 4D reconstruction methods offer a path to spatialize these predictions, independently reconstructing and merging each camera stream fails to enforce cross-view consistency. This limitation is particularly detrimental when combining moving robot-mounted cameras with fixed external views. To address this, we introduce RoGSW4RLD, a feed-forward framework that lifts synchronized multi-camera rollouts into a unified, time-queryable metric 4D Gaussian field. Rather than learning a separate geometric transition model, RoGSW4RLD directly reconstructs the visual future generated by existing world models. Its core innovation is a two-stage architecture: Stage 1 jointly forms the metric 4D field by fusing cross-view evidence with robot-specific articulated geometry and kinematics, while Stage 2 refines the field's geometry and appearance while strictly preserving the initial temporal displacements. Evaluated on 256 held-out DROID episodes, RoGSW4RLD significantly outperforms camera-wise reconstruction with calibrated merging, improving novel-view PSNR by 2.15 dB, reducing depth AbsRel by 47%, and lowering robot displacement error by 61%. These robust gains extend to action-conditioned Cosmos 3 rollouts, demonstrating that predicted video futures can be successfully translated into consistent, spatially queryable 4D metric representations.