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cs.CV2026
EgoAfford: Task-Oriented Affordance Grounding via Egocentric Referring Segmentation
Xinyuan Guan, Feifan Chen, Xinyu Zhan +3
Part-level affordance grounding has advanced the localization of functional object regions associated with elemental actions. Extending this capability to complex tasks calls for c…
cs.CV2026
LaMP: Learning Vision-Language-Action Policy with 3D Scene Flow as Latent Motion Prior
Xinkai Wang, Chenyi Wang, Yifu Xu +7
We introduce \textbf{LaMP}, a dual-expert Vision-Language-Action framework that embeds dense 3D scene flow as a latent motion prior for robotic manipulation.Existing VLA models reg…
cs.CV2025
ArtGen: Conditional Generative Modeling of Articulated Objects in Arbitrary Part-Level States
Haowen Wang, Xiaoping Yuan, Fugang Zhang +4
Generating articulated assets is crucial for robotics, digital twins, and embodied intelligence. Existing generative models often rely on single-view inputs representing closed sta…