QWERTY: Training-Free Motion Control via Query-Warped Video Diffusion Transformers

Video diffusion transformers (DiTs) generate high-fidelityand temporally coherent videos, yet motion control remains implicit,primarily relying on text prompts. As a result, achieving desired motionoften requires extensive prompt engineering and repeated resampling.While fine-tuning models with additional spatial prompts (e.g., bound-ing boxes or point trajectories) enables explicit control, it demands sub-stantial data curation and computation, and may compromise the gener-ative capabilities of pretrained models. Consequently, training-free mo-tion control using such spatial prompts has been explored in U-Net–basedvideo diffusion models, but remains largely unexplored for DiTs. We in-troduce Qwerty, a training-free framework that enables flexible motioncontrol in pretrained image-to-video DiTs via user-defined object warpingand optical flow. We carefully manipulate the 3D full attention of DiTsby warping the frame-invariant semantic subspace of queries. We findthat the noise predicted by the query-warped DiT naturally guides thediffusion trajectory toward the desired motion, and further show thatleveraging this noise as self-guidance for latent optimization improvescontrol stability and visual quality. Experiments show that Qwertyachieves the most effective motion control among existing training-freeapproaches on a recent image-to-video DiT, with performance compara-ble to fine-tuning–based methods.