The AI video landscape is crowded. But beneath the hood, most tools rely on a fragmented pipeline: one model for text-to-video, another for style transfer, and a third for in-painting. UniVideo changes the game.
The "Pipeline Problem"
Traditional solutions often chain multiple models together. For example, to generate a video and then edit it, you might pass the output of Model A (Generator) into Model B (Editor).
The Issue: Information loss. Model B doesn't "know" the original prompt or context Model A used. It just sees pixels. This leads to drift—where the edited video loses the character's identity or the scene's consistency.
The UniVideo Solution: Unified Context
UniVideo uses a single MLLM (Qwen2.5-VL) to hold the entire context: the text prompt, the reference images, and the video frames. This means when you ask for an edit, the model keeps the original subject's identity perfectly intact because it's generating the edit conditioned on the original understanding.
| Feature | Traditional Models | UniVideo |
|---|---|---|
| Instruction Following | Limited (Keywords) | Advanced (Natural Language) |
| Video Editing | Separate Model Required | Native / Zero-Shot |
| Multi-Image Prompting | Often Fails / Inconsistent | High Consistency (In-Context) |
| Architecture | Visual Encoder + DiT | MLLM + MM-DiT (Unified) |
Future-Proofing
By betting on a unified architecture, UniVideo scales with the LLM. As Qwen2.5-VL and future MLLMs get smarter, UniVideo's understanding of physics, causality, and storytelling improves automatically, without needing to retrain the video generator from scratch.