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pro vyhledávání: '"Frühstück, Anna"'
Autor:
Shen, Yuan, Ceylan, Duygu, Guerrero, Paul, Xu, Zexiang, Mitra, Niloy J., Wang, Shenlong, Frühstück, Anna
We present a simple, modular, and generic method that upsamples coarse 3D models by adding geometric and appearance details. While generative 3D models now exist, they do not yet match the quality of their counterparts in image and video domains. We
Externí odkaz:
http://arxiv.org/abs/2406.00609
We introduce VIVE3D, a novel approach that extends the capabilities of image-based 3D GANs to video editing and is able to represent the input video in an identity-preserving and temporally consistent way. We propose two new building blocks. First, w
Externí odkaz:
http://arxiv.org/abs/2303.15893
Autor:
Frühstück, Anna, Singh, Krishna Kumar, Shechtman, Eli, Mitra, Niloy J., Wonka, Peter, Lu, Jingwan
While GANs can produce photo-realistic images in ideal conditions for certain domains, the generation of full-body human images remains difficult due to the diversity of identities, hairstyles, clothing, and the variance in pose. Instead of modeling
Externí odkaz:
http://arxiv.org/abs/2203.07293
Autor:
Alfarra, Motasem, Pérez, Juan C., Frühstück, Anna, Torr, Philip H. S., Wonka, Peter, Ghanem, Bernard
This work evaluates the robustness of quality measures of generative models such as Inception Score (IS) and Fr\'echet Inception Distance (FID). Analogous to the vulnerability of deep models against a variety of adversarial attacks, we show that such
Externí odkaz:
http://arxiv.org/abs/2201.13019
Publikováno v:
ACM Transactions on Graphics (SIGGRAPH 2019) 38 (4)
We tackle the problem of texture synthesis in the setting where many input images are given and a large-scale output is required. We build on recent generative adversarial networks and propose two extensions in this paper. First, we propose an algori
Externí odkaz:
http://arxiv.org/abs/1904.12795
Akademický článek
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