🕶️ Walk through a first-person world with your own body motion, and explicitly specify what exists at a given location with an image and pose, including how it evolves over time. Meet AnchorWorld, an embodied egocentric world model. Title: AnchorWorld: Embodied Egocentric World Simulation with View-based Evolution Customization URL: 📝 Overview AnchorWorld generates first-person video controlled by full-body human motion. With "anchor views," it lets you explicitly specify what exists at a given 3D location and how it changes over time. ❓ Challenges Solved Existing world models struggle to supervise full-body motion from egocentric video alone, and define environments only implicitly. They lacked both natural embodied control and localized world customization. 💡 Methodology & Proposed Approach ・Since most of the body is invisible in first person, it uses third-person video as auxiliary supervision to learn body-environment positioning ・An anchor has three parts: an RGB image, a 6-DoF viewpoint pose, and an evolution prompt that specify local appearance and temporal change ・3D RoPE spatially distinguishes multiple anchors, and masked cross-attention enables anchor-specific text control ・It trains in four stages (third-person, first-person, static anchors, dynamic evolution), built on Wan 2.2 TI2V 5B 🎯 Use Cases It applies to embodied VR apps, first-person game environment design, embodied-AI training scenarios, and interactive video generation with localized control. 📊 Experimental Results ・On egocentric static scenes it reaches CLIP-V 0.885 and camera accuracy ATE 0.112m, beating PlayerOne and others ・On egocentric dynamic scenes, text alignment (VideoAlign-TA) is 0.717, far above CaM-Ego's 0.385 ・It generalizes strongly to out-of-distribution UE and real-world scenes with little visual overlap between the initial view and anchors #WorldModel# #EmbodiedAI#

We’ve successfully tested a connection between OpenMind robots and Virtuals ACP. This early test gives embodied AI access to an agent ecosystem that can coordinate, execute, and settle real tasks, hinting at robots as economic participants, not just interfaces. — aGDP