How Does AI Video Style Transfer Work?

Two signals get extracted from the source footage before restyling starts: a depth map for each frame, capturing which parts of the scene are near or far from the camera, and optical flow, the frame-to-frame motion of every pixel (the same signal behind frame interpolation, see how AI frame interpolation works). These are fed into the diffusion model as structural conditioning, similar in spirit to how ControlNet conditions image generation on pose or depth (see what ControlNet is), so the model regenerates each frame constrained to that structure while following the target style.

The text prompt or reference style image controls everything about appearance, color palette, texture, rendering style, while the depth and motion data controls everything about layout and movement, keeping the restyled output tied to what was actually filmed rather than generating a loosely related new scene.

Diffusion is not deterministic: running the exact same source frame through the same restyling prompt twice, with different random noise, can produce two visibly different renderings, close in content but different in fine detail. Restyle frame 1 and frame 2 independently and even a perfectly static shot can flicker, because each frame's fine texture was regenerated from a fresh, uncorrelated draw.

Temporal consistency techniques address this directly: some systems carry information from an already-restyled frame forward as a starting point for the next one, rather than starting each frame from independent noise; others constrain the diffusion process across a short window of frames at once, the same all-frames-together approach behind text-to-video generation (see how AI video generation works), rather than one frame at a time.