The paper introduces GeoNeXt, a framework that repurposes pretrained video generative models for geometry estimation by framing it as a next‑frame prediction task. Unlike prior methods that either train separate depth/normal models or fine‑tune image diffusion backbones, GeoNeXt jointly models images and geometric targets, leveraging the structured knowledge of video models for more data‑efficient learning. Experiments show zero‑shot monocular depth and surface normal estimation that outperforms existing generative approaches and rivals discriminative state‑of‑the‑art methods while using far less training data.
By Haosen Yang, Jifei Song, Zhensong Zhang, Xiatian Zhu, Jiankang Deng
LocaLeJEPA extends Joint-Embedding Predictive Architectures (JEPA) by introducing a manifold-theoretic reformulation that aligns representation learning with non‑Euclidean geometric priors. It incorporates a locally Euclidean distribution‑matching regularizer, LocaLeReg, to stabilize training and scale to large models. Experiments on ImageNet‑1K show that LocaLeJEPA outperforms the original LeJEPA across both hyperbolic and Euclidean backbones, improving linear and k‑NN classification as well as downstream tasks.
By Kartik Tandon, Alessio Borgi, Pavel Snopov, In\'es Garc\'ia-Redondo, Davide D'Ascenzo, Sebastiano Cultrera di Montesano, Fabrizio Silvestri, Pietro Li\`o, Francesca Dominici, Randall Balestriero, Claudio Battiloro
arXiv:2606. 12217v1 Announce Type: cross Abstract: World Action Models (WAMs) offer a promising route for robot manipulation by using video generation models to model future scene evolution before producing control actions.
By Lu Qiu, Yizhuo Li, Yi Chen, Yuying Ge, Yixiao Ge, Xihui Liu
arXiv:2609.35734v2 Announce Type: replace
Abstract: Novel view synthesis from sparse images must reconcile faithful reconstruction of observed regions with plausible completion of unseen content, whi...
By Kerui Ren, Tao Lu, Linning Xu, Changjian Jiang, Mu Huang, Chunhua Shen, Mulin Yu, Bo Dai
arXiv:2602. 03282v2 Announce Type: replace-cross Abstract: A common assumption in representation learning is that globally well-distributed embeddings support robust and generalizable representations.
By Jiwan Chung, Seon Joo Kim
arXiv:2610.22655v1 Announce Type: cross
Abstract: The linear representation hypothesis holds that pretrained vision models encode generative factors as linearly decodable directions in feature space,...
By Nadir Kutluozen, Christian Intern\`o, David Klindt
World Action Models (WAMs) offer a promising route for robot manipulation by using video generation models to model future scene evolution before producing control actions. However, our empirical observations reveal a phenomenon: generating plausible visual futures does not always guarantee the extraction of accurate actions.
arXiv:2607. 05468v1 Announce Type: cross Abstract: World Action Models (WAMs) have shown strong potential for robotic manipulation by jointly modeling visual future dynamics and executable action sequences.
By Jianjun Zhang, Jian Zhu, Taiyi Su, Chong Ma, Zitai Huang, Yi Xu, Hanli Wang
arXiv:2510.03075v4 Announce Type: replace-cross
Abstract: Compositional generalization, the ability to generate novel combinations of known concepts, is a key ingredient for visual generative models....
By Karim Farid, Rajat Sahay, Yumna Ali Alnaggar, Simon Schrodi, Volker Fischer, Cordelia Schmid, Thomas Brox
arXiv:2609.27455v1 Announce Type: new
Abstract: World Action Models (WAMs) jointly model action generation and environment dynamics and are mostly built on pretrained Video Diffusion Models (VDMs). I...
By Xueji Fang, Boqiang Duan, Hua Wu, Jingdong Wang, Guo-Jun Qi
arXiv:2602. 24264v2 Announce Type: replace-cross Abstract: Compositional generalization, the ability to recognize familiar parts in novel contexts, is a defining property of intelligent systems.
By Arnas Uselis, Andrea Dittadi, Seong Joon Oh
The paper introduces GeoSim, a four‑level framework for analyzing how vision‑language models (VLMs) represent low‑level vision tasks. It evaluates hidden‑layer representations across 24 tasks and two VLM paradigms—autoregressive models and diffusion transformers—using global similarity, local geometry, sparse feature decomposition, and topological verification. The study uncovers the organizing principles of low‑level visual representations and highlights their limitations in cross‑task and cross‑model agreement, offering an interpretability lens for assessing latent transferability and diagnosing model‑specific issues.
By Shao-Jun Xia, Huixin Zhang, Zhen Lei, Anlan Sun, Yuner Zhang, Xiaoyang Chen