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LatestSkywork2.0
SkyworkImage → Video

Skywork Releases Open 'World Model' for Playable Video

The new 1.3-billion-parameter model functions as an interactive 'world model,' generating controllable video scenes from a single static image.

Aug 8, 2025
NotableMIT
Matrix-Game 2.0

AI research lab Skywork has released Matrix-Game 2.0, a 1.3-billion-parameter model that generates interactive video from a single static image. Published with a permissive MIT license, the model is described as a "world model," designed to simulate a dynamic environment based on the content of an initial picture.

Unlike typical image-to-video generators that produce a fixed clip, Matrix-Game 2.0 is built for real-time interaction. According to the official release information, users can provide control inputs to direct the action within the generated video, effectively turning a static image into a playable, game-like scene.

This release provides an accessible, open-source tool for developers and researchers exploring simulated environments. By focusing on interactive control rather than just passive video generation, Skywork's model pushes further into the territory of creating dynamic, explorable digital worlds. Its relatively compact size makes it a notable entry in a field currently dominated by much larger, closed-source systems.

Sources

  • Skywork/Matrix-Game-2.0

    Hugging Face

    Visit
OlderMeta Releases DINOv3 ViT-L/16 Vision BackboneMeta AI · Embeddings · last yearNewerZhipu AI Releases Open Vision Model GLM-4.5VZhipu AI · Vision-Language · last year

Get the model

Hugging Face

Specs

Parameters1.3B
Frame rate25 fps
ArchitectureMatrixGame2I2VPipeline
LicenseMIT
Downloads421
Likes304

Can you run it?

Runs on just about anything.

  • BF16 (as published)

    Any modern laptop · 8 GB RAM

    3.6 GB
  • FP8

    Any modern laptop · 8 GB RAM

    2.3 GB
Your machine
  • Precision not published; assuming 16-bit (BF16) weights.
  • Parameter count is an editorial estimate — no weights metadata on Hugging Face.
  • Covers the published checkpoint; text encoders and VAE loaded alongside may add a few GB (many runtimes offload them to CPU).

Estimated from the parameter count. Assumes an 8K context and ~1 GB runtime overhead; actual needs vary. How we estimate


Modalities

Image → Video

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