The Hidden Bottlenecks of AI Video Generation: Why Consistency Beats Complexity

When diving into local AI video generation, it quickly becomes clear that the real bottleneck isn’t about making a pretty image—it’s about maintaining a coherent state over time. While models like Minimax H3 might offer uncensored capabilities, the actual video generation process reveals some serious technical hurdles.

The `fl2v` model is particularly interesting because it effectively treats short clips as an assisted keyframe system. By using the final frame of one segment as the starting point for the next, you are essentially giving the model a physical state to continue from rather than asking it to remember everything perfectly.

However, relying on `ref2v` to reconstruct identity and situation is much less reliable. If the characters aren’t clearly visible in those transition frames—say they are facing away or partially obscured—the model has too much freedom to reinterpret them.

This leads directly into a fascinating realization: models have what can be called an ‘ease landscape.’ Certain configurations, movements, and spatial relationships appear frequently enough that the model handles them with natural ease. Conversely, less common combinations lead to hallucinations, anatomical errors, or misplaced hands. Because of this, a seemingly simple scene like a fixed camera shot demanding exact relative positions is actually harder than a visually complex one.

Ultimately, starting with these insospechably difficult tasks is counter-intuitively useful. By forcing yourself to control identity, pose, and temporal consistency from the start, you build a skillset that makes standard, conventional AI tasks feel almost insultingly easy later on.