Dynamic Texture Transfer using PatchMatch and Transformers
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 12 of its 22 references that are in the map. First posted on arXiv 1 Feb 2024.
Builds on
12 of 22 references.
References · 12
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- 2020Taming Transformers for High-Resolution Image Synthesiscited by 3,253
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- 2021VideoGPT: Video Generation using VQ-VAE and Transformerscited by 658
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- 2019Controllable Artistic Text Style Transfer via Shape-Matching GANcited by 45
- 2016Awesome Typography: Statistics-Based Text Effects Transfercited by 24
- 2018TET-GAN: Text Effects Transfer via Stylization and Destylizationcited by 19
- 2017Two-Stream Convolutional Networks for Dynamic Texture Synthesiscited by 15
- 2018Context-Aware Text-Based Binary Image Stylization and Synthesiscited by 11
- 2020Learning to Transfer Visual Effects from Videos to Imagescited by 1
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Citing papers per year: 2024: 2; 2025: 1.
3 papers.