DreamForge-World 0.1 Preview: A Low-Compute Real-Time Controllable World Model
arXiv:2606. 30292v1 Announce Type: new Abstract: We present DreamForge-World 0.
Waypoint‑1.5 is a real‑time diffusion world model designed for interactive video generation on consumer‑grade hardware. It is pre‑trained on 100,000 hours of control‑aligned video game footage and can generate playable video conditioned on full keyboard and mouse input. The system offers two resolution variants, distinguishes rendered FPS, latent FPS, and control rate, and includes a detailed data pipeline, architecture, training methodology, and runtime system.
arXiv:2606. 30292v1 Announce Type: new Abstract: We present DreamForge-World 0.
arXiv:2508.13009v5 Announce Type: replace Abstract: Recent advances in interactive video generations have demonstrated diffusion model's potential as world models by capturing complex physical dynami...
arXiv:2607.26694v3 Announce Type: replace Abstract: We present Visko Orbis 1.0, a Live Model for real-time, interactive long video generation. Users can change the prompt at any moment during generat...
arXiv:2607. 18367v1 Announce Type: new Abstract: Unlike conventional video game development, which relies on labor-intensive pipelines for asset production, animation, physics, and programming, video world models generate interactive environments from user inputs instantly.
Magpie is a real‑time generative world‑rendering system designed for interactive games. It decouples gameplay execution from visual generation, allowing designers to define scenes and rules in a game engine while a separate render server produces visual output from the engine’s white‑box frames. This approach preserves gameplay designability and reproducibility, and reduces early prototype dependence on finished visual assets.
arXiv:2607. 19191v1 Announce Type: cross Abstract: We present ABot-World-0, an action-conditioned video world model for real-time, long-horizon closed-loop interaction, supported by a multi-source data infrastructure spanning AAA games, simulation engines, and internet videos to learn controllable world dynamics.
arXiv:2607. 20174v1 Announce Type: cross Abstract: Existing human--object interaction (HOI) video generation methods are largely limited to offline short-video generation with complex driving conditions, making them unsuitable for real-time interactive applications.
arXiv:2607. 03118v1 Announce Type: cross Abstract: We introduce Vidu S1, a real-time interactive video generation model supporting voice control of digital characters.
The paper introduces a large‑scale synthetic data pipeline built on Unreal Engine to generate action‑conditioned, multi‑view video for training world models. The system operates in two stages: real‑time physics simulation records trajectories, then offline rendering produces high‑quality video. It includes a distributed production framework with task partitioning, automated filtering, and a 25‑server cluster, yielding over 2,600 hours of 1080p and 6,000 hours of 720p video from 429 levels and 40 characters.
arXiv:2606. 23743v1 Announce Type: cross Abstract: Modern video diffusion models achieve higher generation quality through scaling, but this also increases inference cost.
arXiv:2608. 14022v1 Announce Type: cross Abstract: Action-conditioned video world models require low-latency causal generation and reliable responses to game-native controls.