How Genie3 Works
Genie 3 represents a breakthrough in world modeling, using autoregressive generation and self-learned physics to create interactive, consistent virtual environments without traditional 3D engines or hard-coded physics.
Each frame is conditioned on user actions and the full prior trajectory—essential for closed‑loop interactivity and consistency over time. This approach enables the model to maintain coherent worlds across extended exploration sessions.
Key Innovation:
The model remembers what it previously generated without explicit programming. DeepMind researchers discovered the model can recall details from up to a minute ago, enabling return navigation to previously visited locations with preserved object states.
Unlike NeRFs or Gaussian Splatting that require explicit 3D geometry, Genie3 renders and updates scenes frame‑by‑frame through learned representations, favoring dynamics and editability over geometric precision.
- • No pre-built 3D models or meshes needed
- • Direct frame generation from learned world understanding
- • Flexible scene manipulation without geometry constraints
- • Trade-off: geometric accuracy for richer dynamics
Genie 3 doesn't rely on hard-coded physics engines. Instead, it teaches itself how the world works—how objects move, fall, and interact—by reasoning over long time horizons and remembering what it has generated.
Simulated Physics:
Water dynamics, light shifts, wind effects, cascading lava, collision responses
Learned Behaviors:
Object permanence, gravity effects, material properties, environmental interactions
Evolution: Genie 1 → Genie 2 → Genie 3
First generative interactive environment trained from Internet videos.
- • Unsupervised learning from unlabeled videos
- • Spatiotemporal tokenizer for video compression
- • Autoregressive dynamics model
- • Learned latent action space from video sequences
- • Proof of concept for controllable world generation
Single image to playable world transformation with extended consistency.
- • Single image → interactive world generation
- • 10-20 seconds of consistent generation
- • Long-horizon memory capabilities introduced
- • Diffusion-based world model sampled autoregressively
- • Multiple perspective support (first-person, isometric)
Real-time interactive worlds with dynamic text control and AGI-scale consistency.
- • Real-time generation at 24 FPS, 720p resolution
- • Multiple minutes of world consistency
- • Text-prompted dynamic world events
- • ~1 minute visual memory for out-of-view content
- • Built on Veo 3 foundations for physics understanding
- • First real-time interactive world model from DeepMind
Genie 2 vs Genie 3 Comparison
Side-by-side comparison showcasing the dramatic improvements in quality, consistency, and interactivity from Genie 2 to Genie 3.
Notice the enhanced visual quality, improved consistency, and smoother motion dynamics in Genie 3.
The evolution from Genie 2's capabilities to Genie 3's real-time performance and extended memory.
Technical Architecture
- • Text prompt initialization
- • User action interpretation
- • Dynamic text event injection
- • Trajectory history maintenance
- • Frame-by-frame generation
- • Consistency enforcement
- • Memory buffer (~1 minute)
- • Physics simulation layer
- • 720p resolution rendering
- • 24 FPS real-time stream
- • Multi-perspective support
- • Seamless transitions
- • SIMA agent compatibility
- • Standard navigation inputs
- • Text command interface
- • Future API/SDK planned
Genie 3 builds upon years of research in world models, video generation, and interactive environments. The technical paper is pending public release, but the demonstrated capabilities show significant advances in consistency, interactivity, and physical understanding.
"We think world models are key on the path to AGI, specifically for embodied agents, where simulating real world scenarios is particularly challenging."
— Jack Parker-Holder, DeepMind Research Scientist