Continuous FunctionStart learning

Foundation Lab

Video World Models

Merges generative modeling with dynamics modeling

Concept 97 of 100Generative ModelsPhase 13
Inspect equationAll Foundations
#97VideoWMGenerative Models
key equation
pθ(x1:T∣c)=∏tpθ(xt∣x<t,c)p_\theta(x_{1:T}|c) = \prod_t p_\theta(x_t | x_{<t}, c)
Reading map and next steps

Selected Foundation Object

Keep the equation fixed; move through the evidence.

Concept 97 of 100VideoWMGenerative Models / Phase 13: Cutting-edge 2024-2025 research
Current question

Not just "video generation" but learned physics engine

pθ(x1:T∣c)=∏tpθ(xt∣x<t,c)p_\theta(x_{1:T}|c) = \prod_t p_\theta(x_t | x_{<t}, c)
PredictionCommit before tracing the equation.

Ask what should change under a concrete input, then trace that expectation through the equation.

EvidenceCompare the equation and source.

Use the key equation and canonical papers as the available witnesses, without implying that a runnable panel exists.

InvariantName what survives notation changes.

The useful learning product is the reusable mechanism you can carry into another model, paper, or engineering tradeoff.

Next moveContinue through the atlas.

Use prerequisites, dependents, and semantic links to repair the next gap without leaving the object behind.

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Why It Matters for Modern Models

  • Merges generative modeling with dynamics modeling
  • Precursor to general planning/agents
  • Sora shows emergence of 3D consistency, object permanence

What Tutorials Skip

What is still poorly explained in textbooks and papers:

  • Not just "video generation" but learned physics engine
  • Emergent properties: camera control, object tracking, causality
  • Can imagine "what happens if" for planning

Visualization Status

Core Math (Optional Deep Dive)

If you want intuition first, start with the key equation and cited sources. Come back here for the full walkthrough.

Key Equation
pθ(x1:T∣c)=∏tpθ(xt∣x<t,c)p_\theta(x_{1:T}|c) = \prod_t p_\theta(x_t | x_{<t}, c)

Video as learned dynamics. Autoregressive:

pθ(x1:T∣c)=∏t=1Tpθ(xt∣x<t,c)p_\theta(x_{1:T}|c) = \prod_{t=1}^T p_\theta(x_t | x_{<t}, c)

Diffusion over latent zz:

min⁡θEt,ϵ[∥ϵ−ϵθ(zt,t,c)∥2]\min_\theta \mathbb{E}_{t,\epsilon}[\|\epsilon - \epsilon_\theta(z_t, t, c)\|^2]

Video generators = learned simulators of physical world.

Canonical Papers

Video generation models as world simulators

OpenAI2024OpenAI
Read paper →

Connections

Prerequisites

∂DiffusionℤVAEs

Next Moves

Choose the next question to carry this object forward.