# Video generation


📂 model-architectures

## MODEL ARCHITECTURES

# Video generation

Generate spatial and temporal structure together using frame, patch, or latent video representations with cross-frame computation.

## MENTAL MODEL

Image generation plus a time axis, and therefore a much larger consistency, memory, motion, and evaluation problem.

## DATA FLOW

- Text, image, or video condition

- Spatiotemporal representation

- Temporal diffusion or token generator

- Video decoder / upsampler

- Frames + audio alignment checks

## How it trains

Objectives include spatiotemporal denoising in pixel or latent space and next-token prediction over visual codes. Conditioning can include text, key frames, motion, masks, or camera controls.

## How inference runs

The system samples a clip jointly or in windows, often with cascaded refinement. Longer duration, higher resolution, and stronger temporal coherence all increase compute and memory pressure.

## Strengths

- •
Text- or image-conditioned motion synthesis

- •
Video extension, interpolation, and editing

- •
Latent representations can amortize the cost of raw pixels

## Trade-offs

- •
Identity and object persistence can drift over time

- •
Generation and human review are expensive

- •
Physics, timing, readable text, and synchronized audio require dedicated tests

## Use it when

- Short creative clips or controlled transformations

- Temporal failure modes are included in the acceptance rubric

- Asynchronous generation is acceptable

## Avoid or challenge it when

- A real-time deterministic renderer is required

- Continuity or physical correctness cannot be reviewed

- The workflow cannot support provenance and consent controls

## Illustrative published families

- • Video Diffusion Models research family

- • Latent video diffusion and token-based video generators

## Commonly combines with

## PRIMARY REFERENCES

## Model Architectures

## Model Architectures