Open interactive world models you can run or train: Matrix-Game 3.5 (720p, long-horizon memory, first/third person, 40 GB GPU) and ForgeWM (fully open recipe, real-time keyboard/mouse control, trained on 8 GPUs)
Two open releases extend the open world-model landscape (C3-B399-01). Matrix-Game 3.5 (Riemann Dynamics; HF weights apache-2.0, July 2026; repo Sep 2026) (R1, R2): memory-augmented interactive world model for 720p long-horizon camera-controllable video in first-person and third-person modes. "Patch Memory" lifts past observations into a 3D memory with geometric alignment and visibility-aware retrieval so revisited places stay consistent; static scene and dynamic subjects are separated, with third-person mode taking 0-4 protagonist reference crops for identity. Built on Wan2.2-TI2V-5B with Depth-Anything-3; 5B base (bidirectional) checkpoints plus a three-step distilled first-person causal checkpoint for real-time streaming. Needs one NVIDIA GPU with >= 40 GB VRAM (704x1280 peaks around 40 GB), Linux, >= 64 GB RAM. ForgeWM (arXiv Aug 2026) (R3, R4): an end-to-end open recipe (bidirectional SFT -> teacher-forced causal AR -> consistency distillation -> on-policy DMD) producing 1/2/4-s...
Structured fields
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"citations": [
{
"url": "https://github.com/Riemann-Dynamics/Matrix-Game-3.5",
"note": "Overview; Requirements; About"
},
{
"url": "https://github.com/asdfo123/ForgeWM",
"note": "Overview; Requirements; About"
}
]
}Evidence claims
- [sourced_fact] Two open releases extend the open world-model landscape (C3-B399-01). Matrix-Game 3.5 (Riemann Dynamics; HF weights apache-2.0, July 2026; repo Sep 2026) (R1, R2): memory-augmented interactive world model for 720p long-horizon camera-controllable video in first-person and third-person modes. "Patch Memory" lifts past observations into a 3D memory with geometric alignment and visibility-aware retrieval so revisited places stay consistent; static scene and dynamic subjects are separated, with third-person mode taking 0-4 protagonist reference crops for identity. Built on Wan2.2-TI2V-5B with Depth-Anything-3; 5B base (bidirectional) checkpoints plus a three-step distilled first-person causal checkpoint for real-time streaming. Needs one NVIDIA GPU with >= 40 GB VRAM (704x1280 peaks around 40 GB), Linux, >= 64 GB RAM. ForgeWM (arXiv Aug 2026) (R3, R4): an end-to-end open recipe (bidirectional SFT -> teacher-forced causal AR -> consistency distillation -> on-policy DMD) producing 1/2/4-step students driven by keyboard/mouse or gamepad; up to 72 FPS at 352x640 (1-step, single H20), 168 ms per chunk; trained on the open GameFactory data (~70 h, Minecraft-style) from a Matrix-Game 2 base, and ported to a gamepad FPS by widening the action interface; weights, code and pre-encoded data released - unlike MG2, which shipped inference only. Practical for worldbuilders: open models now offer persistent-ish explorable scenes for previs/look-dev on a single large GPU, but resolution, duration and domain (game-like data) limit them versus hosted products (Marble C3-B434-01, Decart Oasis C3-B482-01). · locator: Overview; Requirements; About source
Rights
| component | license | hosting | redistribution | modification | training | Evidence and notes |
|---|---|---|---|---|---|---|
| documentation | unknown | permitted | permitted | unknown | unknown | Applies to this original summary only, not to the cited sources. |