no code implementations • 25 Feb 2022 • Yuval Nirkin, Yosi Keller, Tal Hassner
Unlike previous work, we offer a subject agnostic swapping scheme that can be applied to pairs of faces without requiring training on those faces.
1 code implementation • CVPR 2021 • Yuval Nirkin, Lior Wolf, Tal Hassner
We present a novel, real-time, semantic segmentation network in which the encoder both encodes and generates the parameters (weights) of the decoder.
Ranked #6 on Dichotomous Image Segmentation on DIS-TE1
no code implementations • 27 Aug 2020 • Yuval Nirkin, Lior Wolf, Yosi Keller, Tal Hassner
Our approach involves two networks: (i) a face identification network that considers the face region bounded by a tight semantic segmentation, and (ii) a context recognition network that considers the face context (e. g., hair, ears, neck).
1 code implementation • ICCV 2019 • Yuval Nirkin, Yosi Keller, Tal Hassner
We present Face Swapping GAN (FSGAN) for face swapping and reenactment.
1 code implementation • CVPR 2018 • Anh Tuan Tran, Tal Hassner, Iacopo Masi, Eran Paz, Yuval Nirkin, Gerard Medioni
Motivated by the concept of bump mapping, we propose a layered approach which decouples estimation of a global shape from its mid-level details (e. g., wrinkles).
2 code implementations • 22 Apr 2017 • Yuval Nirkin, Iacopo Masi, Anh Tuan Tran, Tal Hassner, Gerard Medioni
To this end, we use the Labeled Faces in the Wild (LFW) benchmark and measure the effect of intra- and inter-subject face swapping on recognition.