Search Results for author: Akin Caliskan

Found 5 papers, 0 papers with code

LiP-Flow: Learning Inference-time Priors for Codec Avatars via Normalizing Flows in Latent Space

no code implementations15 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.

Face Model

Multi-person Implicit Reconstruction from a Single Image

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.

Multi-View Consistency Loss for Improved Single-Image 3D Reconstruction of Clothed People

no code implementations29 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.

3D Human Shape Estimation 3D Reconstruction

Learning Dense Wide Baseline Stereo Matching for People

no code implementations2 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.

Data Augmentation Stereo Matching

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