Suchergebnisse
Suchergebnisse:
20. Juli 2022 · The official repo for CM-GAN (Cascaded Modulation GAN) for Image Inpainting. We introduce a new cascaded modulation design that cascades global modulation with spatial adaptive modulation for better hole filling. We also introduce an object-aware training scheme to facilitate better object removal. CM-GAN significantly improves the ...
Gaurav Parmar, Krishna Kumar Singh , Richard Zhang , Yijun Li , Jingwan (Cynthia) Lu , Jun-Yan Zhu SIGGRAPH, 2023 | | Spatially-Adaptive Multilayer Selection for GAN Inversion and Editing
22. März 2022 · We propose cascaded modulation GAN (CM-GAN), a new network design consisting of an encoder with Fourier convolution blocks that extract multi-scale feature representations from the input image with holes and a dual-stream decoder with a novel cascaded global-spatial modulation block at each scale level. In each decoder block, global ...
- arXiv:2203.11947 [cs.CV]
- 32 pages, 19 figures
We propose cascaded mod-ulation GAN (CM-GAN), a new network design consisting of an encoder with Fourier convolution blocks that extract multi-scale feature repre-sentations from the input image with holes and a dual-stream decoder with a novel cascaded global-spatial modulation block at each scale level .
23. Okt. 2022 · We propose cascaded modulation GAN (CM-GAN), a new network design consisting of an encoder with Fourier convolution blocks that extract multi-scale feature representations from the input image with holes and a dual-stream decoder with a novel cascaded global-spatial modulation block at each scale level.
21. Okt. 2022 · To seek a better way to inject global context into the missing region in inpainting, we investigate a new modulation scheme by cascading global and spatial modulations. We propose Cascaded Modulation GAN (CM-GAN), a new generative network that can synthesize better holistic structure and local details, cf. Figs 1 and 5.
14. März 2022 · We demonstrate the setup by combining a full body GAN with a dedicated high-quality face GAN to produce plausible-looking humans. We evaluate our results with quantitative metrics and user studies.
