WiFlow introduces a method for estimating optical flow using WiFi channel state information (CSI) instead of camera frames, addressing privacy and lighting issues. The approach includes a CSI-based flow estimator, a preprocessor evaluation, and three model architectures balancing accuracy and complexity. Additionally, the authors provide the first dataset for training and evaluating CSI-based optical flow estimators, along with experimental insights into key design elements.
arXiv:2512. 04966v2 Announce Type: replace-cross Abstract: Accurate channel state information (CSI) underpins reliable and efficient wireless communication.
By Guangming Liang, Mingjie Yang, Dongzhu Liu, Paul Henderson, Lajos Hanzo
World Action Models (WAMs) are able to leverage pretrained video generators for both world modeling and action prediction. However, directly leveraging such video generators for control raises a new challenge: how to represent actions in a suitable form that aligns with pretrained video generators while carrying enough motion cues for accurate control.
arXiv:2507.13628v3 Announce Type: replace
Abstract: Separating moving and static objects from a moving camera viewpoint is essential for 3D reconstruction, autonomous navigation, and scene understand...
By Masahiro Ogawa, Qi An, Atsushi Yamashita
FreeFlow is a hierarchical transformer for optical flow estimation that eliminates traditional flow-specific inductive biases such as correlation volumes and feature warping. It relies on a single feed-forward encoder–decoder architecture that incorporates window attention, shifted-window attention, and reduced-resolution global attention. This design allows the model to scale with capacity while achieving state‑of‑the‑art accuracy on Sintel, KITTI‑2015, and Spring benchmarks, all while remaining memory efficient at 1080p inference.
arXiv:2607. 10140v1 Announce Type: cross Abstract: Existing optical flow methods broadly follow two paradigms: iterative optimization and diffusion-based estimation.
By Yuang Meng, Chenyang Wu, Xianshun Liu, Chun-Le Guo, Zichen Liang, Lina Lei, Jie Liang, Hui Zeng, Chongyi Li, Lei Zhang