MUGEN is a large‑scale real‑world dataset of over 1,300 hours of 4K panoramic videos with rich semantic and geometric annotations, designed to support interactive 360° world exploration. Wan360 is a camera‑controllable panoramic video generation model built on MUGEN, featuring ERP‑aware components (periodic longitude RoPE, ERP‑aware padding, random roll yaw) and a panoramic Plücker embedding for camera motion. Together, they address gaps in data and model support for immersive, temporally coherent 360° video generation along user‑specified camera trajectories.
By Jiaming Tan, Zhen Li, Shuwei Shi, Minggui Teng, Siqi Yang, Yuwei Wu, Bo Zheng, Chuanhao Li, Kaipeng Zhang
arXiv:2609.14462v1 Announce Type: new
Abstract: Interactive video world models must maintain broad scene context under camera motion while producing high-fidelity observations with low latency. Exist...
By Jiaming Tan, Mingliang Zhai, Zhen Li, Yuwei Wu, Chuanhao Li, Kaipeng Zhang
arXiv:2607. 00832v1 Announce Type: cross Abstract: A single panorama captures the full visual sphere from one camera center, yet confines users to looking around in place without enabling true scene exploration.
By Zhenjia Li, Jinrang Jia, Yifeng Shi
UniTrackPLA introduces a unified panorama-language-action model that simultaneously handles instruction‑guided navigation and dynamic person tracking for embodied robots. Its Panoramic‑Aware Encoding preserves azimuthal and temporal structure, allowing a shared vision‑language backbone to generate continuous waypoint chunks for both tasks. The model also employs World‑Action Consistency to predict future visual states and verify waypoint prefixes, enabling reliable action reuse and replanning when inconsistencies arise. A new OmniTrackNav‑Bench dataset and extensive real‑world experiments demonstrate significant performance gains over prior methods.
By Pengfei Qi, Haoran Lin, Sizhuang Chen, Kai Luo, Sirui Zhang, Xinqi Liu, Fei Cheng, Wenrui Chen, Liming Yin, Kailun Yang
We present MoVerse, a real-time video world model that creates an interactively navigable scene from a single narrow-field-of-view image. This setting is challenging because the input observes only a small fraction of the environment, while interactive roaming requires a complete surrounding world, persistent geometry, controllable camera motion, and temporally coherent high-fidelity observations.
DiffWAM is a geometry‑conditioned navigation world‑action model that transforms predictive features from a frozen video foundation model into continuous camera trajectories, eliminating the need for future‑video synthesis and multi‑frame reconstruction during deployment. Its Grid‑Motion module preserves spatial‑temporal motion associations, while Latent2Pose grounds them with first‑frame geometry to recover metrically meaningful 3D motion. The system, complemented by FastDreamer for asynchronous trajectory handoff, achieves a trajectory RMSE of 0.3492 m and a 74.40 % endpoint success rate on the DiffWAM‑1000 benchmark, with real‑world tests showing complex UAV behaviors and an onboard implementation reaching 1.08 s latency on NVIDIA Jetson AGX Thor.
By Mo Zhu, Yuze Wu, Xijie Huang, Xiao Cui, Fei Gao, Xin Zhou