arXiv:2206.09210
공식 코드 · Papers with Code
Multi-Modality Image Inpainting using Generative Adversarial Networks
원문 초록
Deep learning techniques, especially Generative Adversarial Networks (GANs) have significantly improved image inpainting and image-to-image translation tasks over the past few years. To the best of our knowledge, the problem of combining the image inpainting task with the multi-modality image-to-image translation remains intact. In this paper, we propose a model to address this problem. The model will be evaluated on combined night-to-day image translation and inpainting, along with promising qualitative and quantitative results.
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CycleVAR: Repurposing Autoregressive Model for Unsupervised One-Step Image Translation
LBM: Latent Bridge Matching for Fast Image-to-Image Translation
Cora: Correspondence-aware image editing using few step diffusion
Unpaired Image-to-Image Translation for Segmentation and Signal Unmixing
Paired and Unpaired Image to Image Translation using Generative Adversarial Networks
Image-to-Image Translation with Diffusion Transformers and CLIP-Based Image Conditioning
토론
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