Equilibrium Forcing: Adaptive Video Generation Without Noise Conditioning

A novel approach known as Equilibrium Forcing (EqF) has been developed for generating videos, designed to address the shortcomings of traditional autoregressive methods based on Diffusion and Flow Matching. These established techniques depend on inflexible training goals and fixed sampling schedules, limiting their ability to adapt during inference. By removing noise level conditioning, EqF streamlines video denoising generative models, facilitating modular designs for training and inference that separate the learning of the denoising field from sampling. This adaptability allows inference algorithms to function in a closed loop, responding to sample feedback, thus enhancing video quality and consistency on difficult autoregressive benchmarks. Comprehensive analysis reveals that EqF's data-dependent inference capabilities outperform standard noise level-conditional methods. The research is documented on arXiv with the identifier 2608.14706, under the category of Computer Vision and Pattern Recognition, contributing to ongoing advancements in AI-based video generation, with implications for digital art and media production.