no code implementations • 2 Jun 2024 • Yuan Shen, Duygu Ceylan, Paul Guerrero, Zexiang Xu, Niloy J. Mitra, Shenlong Wang, Anna Frühstück
We demonstrate that it is possible to directly repurpose existing (pretrained) video models for 3D super-resolution and thus sidestep the problem of the shortage of large repositories of high-quality 3D training models.
1 code implementation • CVPR 2023 • Anna Frühstück, Nikolaos Sarafianos, Yuanlu Xu, Peter Wonka, Tony Tung
Our experiments demonstrate that VIVE3D generates high-fidelity face edits at consistent quality from a range of camera viewpoints which are composited with the original video in a temporally and spatially consistent manner.
2 code implementations • CVPR 2022 • Anna Frühstück, Krishna Kumar Singh, Eli Shechtman, Niloy J. Mitra, Peter Wonka, Jingwan Lu
Instead of modeling this complex domain with a single GAN, we propose a novel method to combine multiple pretrained GANs, where one GAN generates a global canvas (e. g., human body) and a set of specialized GANs, or insets, focus on different parts (e. g., faces, shoes) that can be seamlessly inserted onto the global canvas.
1 code implementation • 31 Jan 2022 • Motasem Alfarra, Juan C. Pérez, Anna Frühstück, Philip H. S. Torr, Peter Wonka, Bernard Ghanem
Finally, we show that the FID can be robustified by simply replacing the standard Inception with a robust Inception.
1 code implementation • 29 Apr 2019 • Anna Frühstück, Ibraheem Alhashim, Peter Wonka
We tackle the problem of texture synthesis in the setting where many input images are given and a large-scale output is required.