Hugging Face Trending Papers
ECHO: Embodied Camera Observations of Human Object Carrying
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arXiv Computer Vision
1d ago
ECHO: Embodied Camera Observations of Human Object Carrying
arXiv:2610.10438v1 Announce Type: new Abstract: Embodied and assistive agents must do more than recognize objects: they must reason about where an object belongs given the layout of an environment an...
arXiv Computer Vision
Sep 18
SceneTeract: Probing and Improving Agent-Aware Activity Reasoning in 3D Indoor Scenes
SceneTeract is a verification interface that separates semantic action understanding from physical feasibility in indoor 3D scenes. It decomposes activities into atomic actions and performs explicit geometric checks to determine executability, providing diagnostic traces for failures. The system reveals widespread functional and accessibility issues in synthetic scenes, shows that existing VLMs over‑predict action feasibility, and improves VLM performance through post‑training with verifier feedback, with benefits that generalize to real‑world scenes.
Hugging Face Trending Papers
1d ago
Never Look Back: Understanding Persistence in 3D Object Memory from Egocentric Videos
As we move through the world and carry out everyday tasks, we encounter objects that may become relevant only later. We are capable of recalling where we left something or what was inside a container,...
arXiv Computer Vision
1d ago
Never Look Back: Understanding Persistence in 3D Object Memory from Egocentric Videos
arXiv:2610.10538v1 Announce Type: new Abstract: As we move through the world and carry out everyday tasks, we encounter objects that may become relevant only later. We are capable of recalling where...
arXiv Computer Vision
Sep 17
Track, Articulate, Act: Generating Articulation from Casual Human Videos
arXiv:2609.19119v1 Announce Type: new Abstract: Human videos contain rich causal evidence for robot manipulation: they reveal how hand motion induces object motion and produces task-relevant changes...
arXiv AI
Jun 29
EXPLORE-Bench: Egocentric Scene Prediction with Long-Horizon Reasoning
arXiv:2603. 09731v3 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) are increasingly considered as a foundation for embodied agents, yet it remains unclear whether they can reliably reason about the long-term physical consequences of actions from an egocentric viewpoint.