no code implementations • 13 Mar 2024 • Abhishek Saroha, Mariia Gladkova, Cecilia Curreli, Tarun Yenamandra, Daniel Cremers
Scene stylization extends the work of neural style transfer to three spatial dimensions.
no code implementations • 16 May 2023 • George Eskandar, Youssef Farag, Tarun Yenamandra, Daniel Cremers, Karim Guirguis, Bin Yang
Moreover, we employ an unsupervised latent exploration algorithm in the $\mathcal{S}$-space of the generator and show that it is more efficient than the conventional $\mathcal{W}^{+}$-space in controlling the image content.
no code implementations • 9 Dec 2022 • Mohammed Brahimi, Bjoern Haefner, Tarun Yenamandra, Bastian Goldluecke, Daniel Cremers
We propose an end-to-end inverse rendering pipeline called SupeRVol that allows us to recover 3D shape and material parameters from a set of color images in a super-resolution manner.
no code implementations • 21 Apr 2022 • Abhishek Saroha, Marvin Eisenberger, Tarun Yenamandra, Daniel Cremers
Finally, we show that our adversarial training approach leads to visually plausible reconstructions that are highly consistent in recovering missing parts of a given object.
no code implementations • 30 Mar 2022 • Tarun Yenamandra, Ayush Tewari, Nan Yang, Florian Bernard, Christian Theobalt, Daniel Cremers
To this end, we learn a signed distance function (SDF) along with our DDF model to represent a class of shapes.
no code implementations • 31 Mar 2021 • Zhenzhang Ye, Tarun Yenamandra, Florian Bernard, Daniel Cremers
While these approaches mainly focus on learning node and edge attributes, they completely ignore the 3D geometry of the underlying 3D objects depicted in the 2D images.
Ranked #12 on Graph Matching on PASCAL VOC
1 code implementation • CVPR 2021 • Tarun Yenamandra, Ayush Tewari, Florian Bernard, Hans-Peter Seidel, Mohamed Elgharib, Daniel Cremers, Christian Theobalt
Our approach has the following favorable properties: (i) It is the first full head morphable model that includes hair.
no code implementations • 24 Oct 2019 • Tarun Yenamandra, Florian Bernard, Jiayi Wang, Franziska Mueller, Christian Theobalt
We consider the problem of inverse kinematics (IK), where one wants to find the parameters of a given kinematic skeleton that best explain a set of observed 3D joint locations.