Technical

Text-to-Video Generation: From Sora to Veo

A comprehensive guide to AI video generation models in 2025.

VI
Vijayakumar S
May 15, 202513 min read
Text to Video Generation Process

The Video Generation Explosion

2025 saw text-to-video mature from novelty to production-ready. Models now generate minutes of coherent, high-resolution video with consistent characters and physics.

Leading Models

  • Sora (OpenAI): 60s videos, 1080p, remarkable physics and consistency
  • Veo (Google): 90s videos, 4K, best for cinematic quality
  • MovieGen (Meta): 120s videos, with audio generation
  • Kling 2.0: Open weights, 30s videos

Architecture Deep Dive

All leading models use some variant of Diffusion Transformers (DiT) for video:

  • Space-time patches: Video as 3D volume (height, width, time)
  • 3D attention: Temporal attention + spatial attention
  • Causal masking: Prevents future frames from leaking

Key Capabilities in 2025

Character Consistency

Models maintain character appearance across multiple clips using reference images or text descriptions.

Camera Control

Natural language camera directives:

"A dolly zoom slowly pushing in on the main character"
"Low angle shot tracking the runner"
"Drone flyover of a futuristic city at sunset"

Physics Simulation

Water, smoke, cloth, and rigid body physics appear realistic. Sora particularly excels at object permanence.

Audio Generation

MovieGen and Veo generate synchronized sound effects and ambient audio.

Implementation Example

import openai

client = openai.OpenAI()

video = client.videos.generate(
    model="sora",
    prompt="A majestic elephant walking through a fantasy forest with glowing mushrooms",
    duration=30,  # seconds
    resolution="1080p",
    camera="tracking shot, slow motion",
    negative_prompt="blurry, distorted, low quality"
)

video.save("elephant_forest.mp4")

Use Cases

  • Marketing: Generate product videos from scripts
  • Education: Create visual explanations of concepts
  • Prototyping: Storyboard scenes before filming
  • Gaming: Generate cutscenes and environmental footage

Limitations

  • Still struggles with precise object manipulation
  • Numbers and text often garbled
  • Long-range temporal consistency beyond 60 seconds
  • Compute cost (~$1 per second of video)
VI
Vijayakumar S
AI Engineer 路 ML Enthusiast

Passionate about building intelligent systems, speech synthesis, and LLM applications. Writing about the tools and ideas shaping the next decade of software.