비디오-비디오 스타일 변환

Most video-to-video pipelines have two steps. First they extract structure from the source video: depth maps from models such as MiDaS or Depth Anything, edge or line maps, human pose skeletons from tools like OpenPose, and sometimes optical flow, which records how pixels move between frames. Then they generate new frames conditioned on that structure plus a style prompt or reference image. ControlNet, introduced in 2023, made this practical for Stable Diffusion by letting a depth map or pose skeleton steer generation without retraining the base model. The key setting is usually transformation or denoising strength. The source frame is partly noised and then denoised toward the new style. Low strength stays close to the original but changes little. High strength gives a bolder style, but the output starts to drift from the source and flicker between frames. Depth conditioning keeps room layout and object placement. Pose conditioning keeps human movement. Edge maps keep fine outlines. There are three broad approaches. Per-frame diffusion with ControlNet relies on shared seeds and cross-frame attention to limit flicker. Keyframe methods restyle a few frames carefully and propagate that look to the rest, as EbSynth does with patch-based propagation. Research methods such as Rerender-A-Video and TokenFlow push further in that direction. Native video models that accept an input video, which Runway's Gen-1 offered in 2023, handle time inside the model. A common misconception is that restyling anonymizes people or turns footage into a new, freely usable work. It does neither automatically. Gait, body shape, voice and setting often stay identifiable, likeness and publicity rights can still apply, and the underlying footage keeps its copyright. Responsible use means filming your own material or getting permission, and disclosing that the footage was transformed.