The paper introduces SemTAC, an encoder trained with a temporal-neighborhood contrastive loss to preserve local consistency in human motion time series, enabling inference at temporal scales beyond training. SemTAC outperforms four baselines on a five-step motion sequence discrimination task, especially on truncated sequences where it achieves a median F1 of 0.77 versus 0.27–0.70 for others. Its representations form interpretable geometric trajectories, surpassing a no-structure baseline on a displacement‑smoothness metric.
By Louren\c{c}o A. Rodrigues, Felix Putze
arXiv:2606. 13817v1 Announce Type: cross Abstract: World models in robot learning predict future states from visual observations and actions, enabling agents to reason about the consequences of their controls.
By Yitao Jiang, Luyang Zhao, Muhao Chen, Devin Balkcom
WinoTS introduces a wavelet‑based self‑distillation framework for time‑series models that uses time‑frequency augmentations to create multi‑scale structural views, avoiding distortion of signal dynamics. The method outperforms state‑of‑the‑art baselines in long‑term forecasting, cross‑domain zero‑shot transfer, and unsupervised anomaly detection, and linear probing on frozen representations often beats fully supervised training from scratch. Ablation studies show WinoTS is architecture‑agnostic and demonstrates that time‑frequency transformations offer a principled alternative to vision‑style spatial augmentations.
By Noam Major, Kathy Razmadze, Yoli Shavit
The paper introduces MESSENGER, a memory‑enhanced sequential scene flow estimation framework that uses a memory buffer to store past flow estimates and latent states. By retrieving and correlating these stored flows for each new frame, the method performs next‑frame forecasting and incorporates an uncertainty‑aware reweighting module to reduce error accumulation. Experiments on nuScenes and Argoverse 2 show significant improvements, reducing EPE3D by 71.6% and 67.7% respectively in long‑horizon predictions.
By Jiuming Liu, Jianing Li, Mengmeng Liu, Hongyang He, Hesheng Wang, Per Ola Kristensson
The paper introduces VANE, a self‑supervised visual pretraining framework that learns from next‑frame prediction in embedding space. It uses a causal encoder to summarize past observations, a state predictor to infer the representation of the entire next frame, and a lightweight generative decoder to reconstruct fine‑grained spatial detail. By applying the same objective to both videos and still images through a local‑to‑global crop sequence, VANE achieves state‑of‑the‑art performance on anticipation, recognition, and dense perception benchmarks, notably outperforming V‑JEPA on EPIC‑KITCHENS‑100 action anticipation.
By Yifeng Yao, Jinghan Li, Zhihao Zhang, Yajiao Xiong, Yang Jin, Kun Xu, Huishuai Zhang, Dongyan Zhao
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.