Image-to-video generation is one of the most fascinating applications of generative AI. Let me break down how it works under the hood.
The Core Architecture
Modern image-to-video models are built on diffusion transformers (DiT). Unlike the original U-Net diffusion models used for images, DiTs use transformer blocks to process video latents.
Latent Space
Videos are first encoded into a compressed latent space using a VAE (Variational Autoencoder). A 512x512 video at 24fps becomes a much smaller tensor that the model can work with efficiently.
Original: [frames × height × width × channels]
Latent: [frames/4 × height/8 × width/8 × latent_dim]
Temporal Attention
The key innovation is temporal attention layers. These allow the model to maintain consistency across frames:
- Spatial attention: Each frame attends to itself (same as image generation)
- Temporal attention: Each spatial position attends across time
- Cross-attention: Both spatial and temporal features attend to the text/image conditioning
Motion Modeling
The model learns motion patterns from training data. When you provide a reference image, the model:
- Encodes the image into latent space
- Predicts how each region should move based on the text prompt
- Generates intermediate frames that maintain the subject's identity
- Decodes back to pixel space
Practical Implications
Understanding the architecture helps you write better prompts:
- Be specific about motion: "camera slowly pans right" gives the temporal attention a clear signal
- Maintain scene consistency: The model preserves what's in your reference image
- Lighting matters: Describe the lighting in your prompt to avoid flickering
Tools Using This Technology
Several platforms have made this accessible:
- PopcornAI - focused on creative video and image generation
- Runway, Pika, Kling - each with their own model variants
The quality gap between open-source and commercial models is closing fast. Models like CogVideoX and Open-Sora demonstrate that open research is keeping pace.
What's Next?
The next frontier is controllable generation - being able to specify exact camera paths, character movements, and scene transitions. We're also seeing progress in longer coherent generation beyond the current 3-10 second limitation.
If you're interested in the technical details, I recommend reading the CogVideoX and SVD papers. They're well-written and accessible.
