Breaking the Uniformity Trap: Scaling Video Diffusion Model via SplitMoE
Why video AI models waste computing power trying to treat all scenes equally
Video generation models trained with Mixture-of-Experts—a technique that lets AI systems use different specialized components for different tasks—usually try to balance the workload evenly across all components. This backfires on video: because videos have repeated patterns and uneven semantic content, forcing uniform expert usage fractures related visual information across disconnected components. A new approach called SplitMoE splits experts into two roles—some specialized for high-level meaning, others for flexible visual details—letting the model naturally group similar content instead of scattering it arbitrarily.
Video generation is computationally expensive, and most current approaches waste resources by preventing experts from specializing in what they're actually good at. SplitMoE generates higher-quality videos while using the same amount of computing power, and it learns faster during training. As video AI models grow larger and more capable, this rethink of how to organize expert systems could set the foundation for the next generation of video synthesis tools.