Where Are Video-Generation AIs Today?
From 2024 to 2026, video-generation AIs have quickly stepped onto the practical entry point. For short clips of a few seconds, they increasingly deliver photorealistic textures, consistent lighting, and reasonably natural camera movement in more scenarios. Notably, OpenAI's Sora, creator-friendly Runway, the rising presence of Chinese entrants Kling, and Google's Veo have been the talk of the town.
However, with expectations high, it's important to ground them. The current battleground is production tasks that yield value in short-length formats such as advertising, social media, concept validation, storyboards, and B-roll, rather than making a movie from start to finish. This article organizes what you can do now, where the pitfalls are, and how to use them to make work easier, focusing on four representative models.
A Rough Positioning of the Four Representative Models
- Sora: A direction strong in longer timelines and complex scene understanding. “a sense of world-simulation” is a talking point.
- Runway: Strong for production workflows. Not only text generation but video editing · replacements · style transfer are rich in on-site features.
- Kling: Notable for high-quality outputs. Strong when its depiction of people and visual style align with preferences.
- Veo: Google's video-generation offering. High resolution, long-form, and prompt adherence hold strong expectations.
Rather than asking which is the strongest, it's more practical to view them as excelling in different stages of the workflow. Next, we'll examine important evaluation axes from a practical, on-site perspective.
Evaluation Axes: What to Look For to Tell If It’s Useful
1) Temporal Consistency
Video is not a single frame; it's essential that subjects don't drift across frames. If patterns on clothing, facial features, or background signs vary from frame to frame, that quickly reveals AI glitches. Currently, shorter clips are more stable; longer takes tend to break down more often.
2) Prompt Adherence
How well can it follow shooting instructions like “in the evening backlight, 35mm lens framing, slow dolly-in”? Differences among models are common, and prompt-design skill matters too.
3) Camera Work and Physical Plausibility
Understanding of camera moves, depth of field, motion blur, and other film grammar helps make the result look plausible. Conversely, when objects multiply or fingers melt, it's still a common failure.
4) Production Workflow Integration
Even with high generation quality, if export, versioning, lip-sync, editing, and replacements are weak, it won't get used. Here, the “completeness” as a production tool—like Runway—becomes decisive.
Sora: The Symbol of Scene Generation Where World Understanding Has Advanced
Sora is often talked about for its ability to generate longer videos that feel plausible, but the essence is that it can unfold while preserving the relationships between elements inside a scene as time progresses. For example, with a connected layout of characters, backgrounds, props, and light sources as time moves forward.
