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Found 6 papers, 4 papers with code
FSGANv2: Improved Subject Agnostic Face Swapping and Reenactment
Unlike previous work, we offer a subject agnostic swapping scheme that can be applied to pairs of faces without requiring training on those faces.
HyperSeg: Patch-wise Hypernetwork for Real-time Semantic Segmentation
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
Dichotomous Image Segmentation
Real-Time Semantic Segmentation
+1
DeepFake Detection Based on the Discrepancy Between the Face and its Context
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).
FSGAN: Subject Agnostic Face Swapping and Reenactment
We present Face Swapping GAN (FSGAN) for face swapping and reenactment.
Extreme 3D Face Reconstruction: Seeing Through Occlusions
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).
On Face Segmentation, Face Swapping, and Face Perception
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.
