Dynamic Texture Transfer using PatchMatch and Transformers
This addresses a challenging problem in computer vision for applications like video editing and animation, though it appears incremental as it builds on existing techniques like PatchMatch and Transformers.
The paper tackles the problem of automatically transferring dynamic textures from a video to a still image by proposing a two-stage model using PatchMatch and Transformers, achieving state-of-the-art results in dynamic texture transfer.
How to automatically transfer the dynamic texture of a given video to the target still image is a challenging and ongoing problem. In this paper, we propose to handle this task via a simple yet effective model that utilizes both PatchMatch and Transformers. The key idea is to decompose the task of dynamic texture transfer into two stages, where the start frame of the target video with the desired dynamic texture is synthesized in the first stage via a distance map guided texture transfer module based on the PatchMatch algorithm. Then, in the second stage, the synthesized image is decomposed into structure-agnostic patches, according to which their corresponding subsequent patches can be predicted by exploiting the powerful capability of Transformers equipped with VQ-VAE for processing long discrete sequences. After getting all those patches, we apply a Gaussian weighted average merging strategy to smoothly assemble them into each frame of the target stylized video. Experimental results demonstrate the effectiveness and superiority of the proposed method in dynamic texture transfer compared to the state of the art.