SIFT: Self-Imagination Fine-Tuning for Physically Plausible Motion in Video Diffusion Models

Recent advances in video diffusion models have greatly im-proved visual fidelity, yet their generated motions often violate physicalplausibility. We observe a common kinematic failure, “motion entangle-ment”, the unintended coupling of independent motion sources, such ascamera movement and object motion. We identify that this issue stemsfrom data bias and the reconstruction-based training design of diffusionmodels. Training on noisy videos that still retain coarse motion cues in-advertently encourages the model to replicate existing motion without anincentive to learn how to model kinematically-grounded motions. To ad-dress this, we propose a Self-Imagination Fine-Tuning (SIFT) paradigm,which enables the model to learn from its own generated videos ratherthan directly reconstructing real ones, breaking the reconstruction short-cut. We further employ motion-aware discriminative supervision and aprogressive hard-case replay strategy to stabilize and accelerate learning.By leveraging freely-generated text prompts, our method can denselycover a broad motion space, including rare or finely-disentangled scenar-ios that would be costly to collect as video data. Extensive experimentsdemonstrate that our approach substantially improves the physical real-ism, motion disentanglement, and controllability of generated videos.