no code implementations • ICCV 2023 • Umar Iqbal, Akin Caliskan, Koki Nagano, Sameh Khamis, Pavlo Molchanov, Jan Kautz
We propose RANA, a relightable and articulated neural avatar for the photorealistic synthesis of humans under arbitrary viewpoints, body poses, and lighting.
no code implementations • 15 Mar 2022 • Emre Aksan, Shugao Ma, Akin Caliskan, Stanislav Pidhorskyi, Alexander Richard, Shih-En Wei, Jason Saragih, Otmar Hilliges
To mitigate this asymmetry, we introduce a prior model that is conditioned on the runtime inputs and tie this prior space to the 3D face model via a normalizing flow in the latent space.
no code implementations • CVPR 2021 • Armin Mustafa, Akin Caliskan, Lourdes Agapito, Adrian Hilton
We present a new end-to-end learning framework to obtain detailed and spatially coherent reconstructions of multiple people from a single image.
no code implementations • 19 Apr 2021 • Akin Caliskan, Armin Mustafa, Adrian Hilton
We present a novel method to learn temporally consistent 3D reconstruction of clothed people from a monocular video.
no code implementations • 29 Sep 2020 • Akin Caliskan, Armin Mustafa, Evren Imre, Adrian Hilton
This paper introduces two advances to overcome this limitation: firstly a new synthetic dataset of realistic clothed people, 3DVH; and secondly, a novel multiple-view loss function for training of monocular volumetric shape estimation, which is demonstrated to significantly improve generalisation and reconstruction accuracy.
no code implementations • 2 Oct 2019 • Akin Caliskan, Armin Mustafa, Evren Imre, Adrian Hilton
We show that it is possible to learn stereo matching from synthetic people dataset and improve performance on real datasets for stereo reconstruction of people from narrow and wide baseline stereo data.