This work introduces HandWorld, a unified generative framework that focuses on hand-object interaction and jointly models ego-centric videos and hand actions and learns shared cross-domain conditions through a dual-branch condition network that integrates information from both video and action domains.
This survey presents a critical review of VLMs for egocentric video understanding, tracing the progression from conventional recognition architectures to multimodal foundation models and embodied systems, and examines how first-person perception and multimodal foundation models support wearable assistance, robot skill...
This work proposes a new learning paradigm that combines hand-object masked training, which enables robust reasoning from partial hand or object observations, and an HOI-dynamics-aware decoder that explicitly learns hand- and object-centric embeddings through auxiliary predictions of their locations and semantics, enha...
AnyWorld is proposed, a cross-embodiment world modeling framework that expands a single human interaction into diverse robot-native rollouts without paired human-robot demonstrations and enables independent recomposition of embodiment, viewpoint, and scene factors, allowing a single model to generate many robot-domain...
Cheng Chen, J. Bai, Jiacheng Wei et al.· 3 citations
EgoWAM is introduced, a controlled human-robot co-training framework that fixes the policy backbone, action head, and data mixture while varying only the world prediction target, comparing Pixel, DINO, and 3D motion flow.
Baoyu Li, Xi Yin, Mengying Lin et al.· 5 citations
G3Ego, a graph-based framework for egocentric action understanding that uses gaze as a structural cue to identify action-relevant entities in the scene, achieves competitive performance compared with video-based approaches and consistently improves Macro-F1 under class-imbalanced evaluation, while avoiding reliance on...
Marko Haralović, Akash Ramakrishnan, E. T. Martínez· 0 citations
This work introduces ZimaBlue, a scalable framework for learning generalizable World Action Models (WAMs) from large-scale video, and adopts an asynchronous Slow-Fast dual-system architecture to make generative WAMs practical for real-time control.
Xiong-Hao Wu, Yi-Jun Yang, Shi-Long Zhou et al.· 5 citations
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