TL;DR: Editing video via the keyframe control signal transformations.
Abstract
Text-based video editing has recently attracted considerable interest in changing the style or replacing the objects with a similar structure. Beyond this, we demonstrate that properties such as shape, size, location, motion, etc., can also be edited in videos. Our key insight is that the keyframe’s transformations of the specific internal feature (e.g., edge maps of objects or human pose), can easily propagate to other frames to provide generation guidance. We thus propose MagicStick, a controllable video editing method that edits the video properties by utilizing the transformation on the extracted internal control signals. In detail, to keep the appearance, we inflate both the pretrained image diffusion model and ControlNet to the temporal dimension and train low-rank adaptions (LORA) layers to fit the specific scenes. Then, in editing, we perform an inversion and editing framework. Differently, finetuned ControlNet is introduced in both inversion and generation for attention guidance with the proposed attention remix between the spatial attention maps of inversion and editing. Yet succinct, our method is the first method to show the ability of video property editing from the pre-trained text-to-image model. We present experiments on numerous examples within our unified framework. We also compare with shape-aware text-based editing and handcrafted motion video generation, demonstrating our superior temporal consistency and editing capability than previous works. The code and models will be made publicly available.
Pipeline
Left: we store all the attention maps in the DDIM inversion pipeline.
At the editing stage of the DDIM denoising, we then fuse the attention maps with the stored attention maps using the proposed Attention Blending Block.
Right: First, we replace the cross-attention maps of un-edited words~(e.g., road and countryside) with their maps using the source prompt during inversion. As for the edited words (e.g., posche car), we blend the self-attention maps during inversion and editing with an adaptive spatial mask obtained from cross-attention, which represents the areas that the user wants to edit.