  Recent advancements in Diffusion Transformer (DiT) have demonstrated
remarkable proficiency in producing high-quality video content. Nonetheless,
the potential of transformer-based diffusion models for effectively generating
videos with controllable motion remains an area of limited exploration. This
paper introduces Tora, the first trajectory-oriented DiT framework that
integrates textual, visual, and trajectory conditions concurrently for video
generation. Specifically, Tora consists of a Trajectory Extractor~(TE), a
Spatial-Temporal DiT, and a Motion-guidance Fuser~(MGF). The TE encodes
arbitrary trajectories into hierarchical spacetime motion patches with a 3D
video compression network. The MGF integrates the motion patches into the DiT
blocks to generate consistent videos following trajectories. Our design aligns
seamlessly with DiT's scalability, allowing precise control of video content's
dynamics with diverse durations, aspect ratios, and resolutions. Extensive
experiments demonstrate Tora's excellence in achieving high motion fidelity,
while also meticulously simulating the movement of the physical world. Page can
be found at https://ali-videoai.github.io/tora_video.
