💡 The paper presents a comprehensive framework called minWM for converting bidirectional video diffusion models into real-time interactive video world models. The problem addressed is that recent video diffusion foundation models have achieved high-quality video generation but turning them into real-time interactive world models remains challenging due to the need for controllable, causal, and low-latency capabilities.

The method used in minWM is a full-stack open-source framework that provides an end-to-end pipeline to convert existing bidirectional video foundation models into camera-controllable few-step autoregressive world models. This is achieved through fine-tuning and distillation techniques, including causal forcing, causal consistency distillation, and asymmetric DMD. The framework is modular and architecture-extensible, allowing it to be instantiated on different open backbones and adapted to new data distributions, training recipes, and latency targets.

The results of minWM are a real-time interactive video world model that can be controlled by a camera, with low-latency rollout and high-quality video generation. The framework is released with runnable scripts, checkpoints, documentation, and inference code, along with practical ablations on camera trajectory quality, controllability training steps, and minimal batch-size requirements. Overall, minWM provides a reproducible and extensible recipe for building and adapting real-time interactive video world models, making it a valuable contribution to the field of video generation and interactive world modeling.