Murea-Labs / InWorld

**Type what you do, and the world films it.**
Open-source video generation with synchronized sound from text and reference images,
and an agent that turns your actions into a continuing, filmed story.
**English** | 中文
🎬 Demo · 🚀 Quick start · 🧭 Guides · 📚 Citation · ✉️ Contact
## 🎬 Demo
**Text-driven storytelling with Murea-InWorld** · 2 min 19 s ·
Download the original-quality video
https://github.com/user-attachments/assets/2617c0f3-ded2-4a8f-81bb-b77095c92b0e
> [!NOTE]
> The Murea-Fast weights are available on Hugging Face.
> Download `murea-fast.safetensors` and follow the deployment guide
> to verify the checkpoint and set up the LTX-2.5 base components.
## 📄 Abstract
Adapted from our technical report.
The report evaluates Murea-Pro and Murea-Fast; this repository provides Murea-Mind and the
Murea-Fast inference service.
Interactive world models aim to simulate environments that evolve under user actions while
retaining the consequences of past interactions. Extending such worlds to sight and sound
requires persistent entities, changing scene states, and local audiovisual dynamics to
remain coherent across turns. This paper presents Murea-InWorld, an interactive audiovisual
world model that couples explicit world-state management with neural audiovisual generation.
Its world agent, Murea-Mind, maintains characters, objects, locations, and narrative progress,
interprets user actions in context, and compiles planned events into generation conditions.
Observations of generated frames inform subsequent planning and selected state updates,
closing the interaction loop.
Its audiovisual generator, Murea, realizes these events through Omni-Reference Generation,
using visual references to anchor entity appearance and structured descriptions to specify
actions and dialogue. Streaming generation uses modality-specific histories, with longer
audio context for unfinished utterances. Degraded-history training prepares the model for
imperfect rollout context, while sequential MeanFlow and distribution matching distillation
enable few-step synthesis. Branching histories support alternative continuations, and co-op
interaction brings multiple participants' actions into the same world.
We evaluate the generation components in complementary configurations. Murea-Pro achieves
an Overall score of **81.13 on WBench Full-289**, the highest among the compared systems.
Murea-Fast achieves a Total score of **64.98 on OpenS2V-Eval** and an aggregate score of
**69.9 on OS2V-Pro**, an extended evaluation of reference fidelity, attribute binding,
rendering quality, and audio. Murea-Pro and Murea-Fast achieve state-of-the-art performance
on their respective benchmarks, and both models surpass most evaluated commercial systems
in their respective evaluations.
## 📣 News
- **2026-10-17** · Watch the text-driven storytelling demo.
- **2026-10-17** · Murea-Fast weights
 are now available on Hugging Face.
- **2026-10** · First open-source release: the Murea-Fast inference servi