Understanding AI Video Generation Technology

AI video generation has gone from a research curiosity to a practical tool for creators in roughly two years. In late 2022, most generated videos were short, blurry clips of a few seconds. By late 2024, tools like OpenAI’s Sora can produce 20-second photorealistic clips at 1080p, and Runway Gen-3 Alpha is being used by professional film studios for pre-visualisation. Understanding how these systems work helps you use them more effectively and set realistic expectations for what they can and cannot do.

The Core Problem: Video Is Extremely Complex

A single second of 1080p video at 24fps contains 24 frames. Each frame is a 1920×1080 image. That’s roughly 49 million pixel values per second — and the model has to generate all of them in a way that is spatially coherent (each frame looks realistic) and temporally coherent (motion flows naturally between frames, objects don’t flicker, and lighting stays consistent).

Early models largely failed at temporal coherence. You’d generate a clip of a dog running and the dog’s legs would morph and distort between frames. Solving this problem is what separates the generation of 2023 from that of 2024.

Diffusion Models: The Foundation

Most modern video generation tools are built on diffusion models, the same basic technology behind image generators like Stable Diffusion, DALL-E 3, and Midjourney. The principle works in two phases:

Forward diffusion (training time): Take a clean image or video and gradually add random noise to it over many steps until it becomes pure static. The model learns to recognise patterns at each noise level.

Reverse diffusion (inference time): Start with random noise and iteratively remove noise, guided by a text or image prompt, until a coherent image or video emerges.

For video, this process happens across the entire clip at once — the model must learn to denoise not just a 2D image but a 3D volume of frames. This is computationally expensive, which is why early video models were slow and produced short clips.

Latent Diffusion: Making It Practical

Running diffusion directly on raw pixel data at high resolution is impractical. The breakthrough that made tools like Runway and Pika usable was latent diffusion: instead of working in pixel space, the model first compresses the video into a compact latent representation using a variational autoencoder (VAE), runs the diffusion process in that smaller space, and then decodes the result back to pixels.

This is roughly 64x more efficient than pixel-space diffusion. Stable Video Diffusion, Stability AI’s open-source model released in November 2023, uses this approach and can run on a consumer GPU with 16GB VRAM, making it one of the first models accessible to individual developers outside of a cloud API.

Transformers and Sora’s Approach

OpenAI’s Sora, announced in February 2024, introduced a different architecture that’s become influential: the video diffusion transformer (DiT). Instead of the U-Net backbone common in earlier diffusion models, Sora uses transformer blocks to process video data represented as “spacetime patches” — small 3D chunks of video frames.

Transformers scale better with data and compute than U-Nets, which is part of why Sora can handle variable resolutions, aspect ratios, and durations (up to 60 seconds at lower resolutions, or 20 seconds at 1080p). The trade-off is that transformer-based models require enormous compute — Sora is only available via OpenAI’s API and as part of ChatGPT Plus, not as a downloadable model.

Runway Gen-3 Alpha, released in July 2024, also uses a transformer-based architecture and represents a major quality jump from Gen-2. Runway has said Gen-3 was trained on a custom high-quality video dataset curated specifically for cinematic motion.

Text Conditioning: How Prompts Guide Generation

The model needs a way to connect your text prompt to the video it generates. This is done through CLIP or similar vision-language encoders, which map text and visual content into a shared embedding space. Your prompt gets encoded into vectors, and these vectors influence the denoising process at every step — steering the model toward outputs that match your description.

This is why prompt wording matters so much. “A golden retriever runs across a field of tall grass, slow motion, 35mm film, golden hour lighting” gives the CLIP encoder very different signals than “a dog running.” More visual specificity results in more grounded outputs.

Image-to-Video: Motion Prediction

A second major model type generates video from a starting image rather than from text alone. Tools like Pika Labs, Luma AI’s Dream Machine, and Runway’s image motion feature use the starting frame as a strong conditioning signal and predict plausible motion forward in time.

These models tend to produce more stable, coherent results for static scenes, because they have a concrete reference for appearance. They’re excellent for animating product photos, portraits, or concept art — contexts where Sora-style text-only generation might introduce unwanted variation.

What the Models Still Get Wrong

Understanding the failure modes helps you work around them:

Hands and fingers remain difficult. The models learn statistical patterns in training data, and hands are highly variable and articulated — they frequently produce extra fingers, melting joints, or unnatural poses.

Text and legible labels almost always fail. If you generate a scene with a blackboard or a street sign, expect garbled or hallucinated characters.

Physics violations — objects clipping through each other, liquids behaving strangely, inconsistent shadows — are still common, especially in Pika and older Runway outputs. Sora and Gen-3 handle physics significantly better but are not immune.

Long-duration consistency degrades. Generating a 4-second clip is much easier than an 8-second clip. The model has to maintain coherent object identity across more frames, and errors accumulate.

Local vs. Cloud Models

One practical decision is whether to use a cloud-based API model (Runway, Pika, Sora, Kling) or a locally-run open-source model (Stable Video Diffusion, AnimateDiff, CogVideoX).

Cloud models are faster to get started with, regularly updated, and don’t require a GPU. Runway Gen-3 consistently produces the highest-quality results of any cloud tool available to individual creators as of late 2024. The downside is cost — Runway charges credits, and serious production use adds up.

Local models give you full control and no per-generation costs, but require a capable GPU (16-24GB VRAM recommended), technical setup, and you’ll be running model checkpoints that trail the commercial offerings in quality.

Where Things Are Heading

The gap between research and product is closing rapidly. Models announced in late 2024 can handle longer videos, more consistent characters, and better understanding of physical interactions than anything available at the start of the year. Kling, developed by Chinese company Kuaishou, arrived in mid-2024 and demonstrated up to 5-minute video generation at 1080p — a duration that would have been considered impossible just 12 months earlier.

For creators, the practical implication is that the skill of prompting and iterating on AI video outputs is becoming genuinely valuable. The tools will keep improving; the ability to direct them effectively will matter more over time.