AI-driven video creation has rapidly evolved from a research novelty into a powerful new medium. Tools like DeepMind’s Veo 3 or OpenAI’s Sora show how generative models can turn simple text prompts into rich video clips. For example, Google’s Gemini site boasts that Veo 3 can create high-quality, 8-second videos with native audio generation. OpenAI likewise reports that its largest video model (Sora) is capable of generating a minute of high-fidelity video.

This is exciting because, like the AI image boom before it, video generation promises to democratize content creation: anyone can now describe a scene and watch it animated. But the implications go beyond just Veo 3 or Sora — the AI video revolution spans many research labs, startups, and platforms (Google DeepMind, OpenAI, Runway, Pika Labs, Kuaishou, Meta, and more) and raises both creative possibilities and hard challenges.

• How AI Generates Video
• Text-to-Video
• Syncing Audio to Video
• Where are we headed?

How AI Generates Video

Modern AI video models typically build on diffusion and transformer architectures, similar to image generation but extended for time. A common approach used by models (like OpenAI's Sora) is latent diffusion which follows three steps:

1. A video compression network is transitioned so that generation happens in a spatiotemporal latent space.
   • This means the model learns to compress video frames into a lower-dimensional representation that captures motion and appearance.
   • This compression is crucial for handling the high dimensionality of video data (e.g., 30 frames per second at 1280x720 resolution).
2. Diffusion modeling (iteratively denoising) is trained on that compressed representation
3. A Decoder maps these outputs back to pixels.
   • Sora uses a Diffusion Transformer (DiT) over “spacetime patches” of the video latents, blending diffusion with self-attention.

Other systems adapt traditional U-Net-based diffusion (used in images) by adding temporal modeling. For instance, Google’s Imagen Video extends U-Nets into 3D (space+time): each diffusion step alternates spatial operations with temporal attention to ensure frame-to-frame coherence. Imagen Video even uses a cascade of models — a base video diffusion model plus multiple upscalers (Temporal and Spatial Super-Resolution) — to achieve final 1280×768 24 fps output.

Text-to-Video

AI Video Generation models are often multimodal. A text prompt is encoded (via CLIP, T5, GPT, etc.) and injected into the network at each stage. Google’s Imagen Video uses a T5-XXL language model to turn text into embeddings, while other pipelines use CLIP text/image embeddings to align words with visual content. This “multimodal alignment” ensures the generated video matches the prompt’s meaning.

Turning words into motion involves careful prompting and orchestration of scene elements. Prompt engineering can include not just subjects and actions but cinematic directions. Many tools respond to phrases like “tracking shot,” “wide aerial view,” or even lens effects. Generators are sensitive to wording, so adding adjectives (lighting, mood) or verbs (camera moves, actions) can often improve results.

Syncing Audio to Video

Audio generation is another key part of AI video. Models like Veo 3 and Sora now aim to produce synchronized soundtracks, including speech, music, and sound effects which takes the problem into multimodal territory.

Current research explores two main approaches:

1. Post-hoc Audio Alignment – Video Generation Models generate realistic lip movements that match a pre-existing audio track. In this setup, the audio is generated first and then the video is warped or generated to align. This works well for dubbing and short clips.

2. Joint Audio-Visual Generation – Newer systems attempt to generate video and audio simultaneously in a shared latent space, ensuring that every frame and sound event emerges in lockstep. Models like V2A-Mapper (by Meta) show that mapping latent video features into audio features improves realism, but these remain computationally heavy and limited in scope.

In practice, many commercial tools still separate the two processes: create the video with Veo 3, Sora, or Runway, then stitch in audio with a model like ElevenLabs for speech or Suno.ai for music. This pipeline is imperfect but more flexible, letting creators fine-tune narration, tone, and timing.

Where are we headed?

The AI video revolution is still in its early days, but the pace of innovation is accelerating. We have moved from rough 2-second loops to multi-shot scenes with soundtracks. Each new model, like Veo 3 or Sora, raises the bar for what’s possible.

In short, generating a coherent, high-res, realistic video with AI is a much taller order than static images. Each frame adds complexity. Researchers are tackling these challenges with novel architectures, larger models, and better data, but the limitations are still evident in today’s tools as technology continues to evolve.