Introducing Waypoint-1: Real-time interactive video diffusion from Overworld
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The Flow has not summarised this story yet — read it at Hugging Face Blog.
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: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...
Multi-agent interactive world models should not only generate consistent observations, but also maintain world states that persist across agents and evolve across views. Existing autoregressive video diffusion pipelines carry forward observation history as conditioning context, which makes shared state difficult to maintain in multi-agent and multi-view settings.
Diffusion-based video restoration recovers realistic details, but its practical deployment is limited by two efficiency bottlenecks: costly VAE encoding and decoding, and the quadratic cost of full se...
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.
FastVR is a streaming video restoration framework that uses a one‑step diffusion model to achieve strong restoration quality and temporal consistency while processing 1080p video at 11 FPS on a single H20 GPU. It addresses efficiency bottlenecks by combining a lightweight VAE with chunk‑wise causal attention, and improves inference speed and restoration quality through velocity consistency regularization and continuous trajectory learning during training. Experiments demonstrate that FastVR outperforms diffusion baselines in efficiency and achieves state‑of‑the‑art performance on both synthetic and real‑world benchmarks.