Generative World Simulation with DAO and JING
cs.CV
Submitted: 2026-10-14
Updated: 2026-10-14
Comments: XGEN DAO and JING Technical report. Contributors listed alphabetically by surname. Project page: https://xgenlabs.ai/research/generative-world-simulation; Model: https://huggingface.co/XGENlabs/XGEN-JING
Project page: https://xgenlabs.ai/research/generative-world-simulation
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Sources
- Wan: Open and Advanced Large-Scale Video Generative Models
- HunyuanVideo 1.5 Technical Report
- Seedance 2.0: Advancing Video Generation for World Complexity
- LTX-2: Efficient Joint Audio-Visual Foundation Model
- WorldPlay: Towards Long-Term Geometric Consistency for Real-Time Interactive World Modeling
- Infinite Worlds with Versatile Interactions
- ABot-World-0: Infinite Interactive World Rollout on a Single Desktop GPU
- AlayaWorld: Long-Horizon and Playable Video World Generation
- MineWorld: a Real-Time and Open-Source Interactive World Model on Minecraft
- Matrix-Game: Interactive World Foundation Model
- Hunyuan-GameCraft: High-dynamic Interactive Game Video Generation with Hybrid History Condition
- Matrix-game 2.0: An open-source, real-time, and streaming interactive world model
- Yume-1.5: A Text-Controlled Interactive World Generation Model
- Hunyuan-GameCraft-2: Instruction-following Interactive Game World Model
- SyncFlow: Toward Temporally Aligned Joint Audio-Video Generation from Text
- Hand2World: Autoregressive Egocentric Interaction Generation via Free-Space Hand Gestures
- LongLive: Real-time Interactive Long Video Generation
Related papers
- Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
- AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
- Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
- MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
- TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
- A Survey on Efficient Vision-Language-Action Models