Face generation is the task of generating (or interpolating) new faces from an existing dataset.

The state-of-the-art results for this task are located in the Image Generation parent.

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Greatest papers with code

Progressive Growing of GANs for Improved Quality, Stability, and Variation

ICLR 2018 jantic/DeOldify

We describe a new training methodology for generative adversarial networks. The key idea is to grow both the generator and discriminator progressively: starting from a low resolution, we add new layers that model increasingly fine details as training progresses.

FACE GENERATION

Talking Face Generation by Adversarially Disentangled Audio-Visual Representation

20 Jul 2018Hangz-nju-cuhk/Talking-Face-Generation-DAVS

However, when people talk, the subtle movements of their face region are usually a complex combination of the intrinsic face appearance of the subject and also the extrinsic speech to be delivered. We assume the talking face sequence is actually a composition of both subject-related information and speech-related information.

TALKING FACE GENERATION

Look Across Elapse: Disentangled Representation Learning and Photorealistic Cross-Age Face Synthesis for Age-Invariant Face Recognition

2 Sep 2018ZhaoJ9014/High_Performance_Face_Recognition

To this end, we propose a deep Age-Invariant Model (AIM) for face recognition in the wild with three distinct novelties. Benchmarking our model on one of the most popular unconstrained face recognition datasets IJB-C additionally verifies the promising generalizability of AIM in recognizing faces in the wild.

AGE-INVARIANT FACE RECOGNITION FACE GENERATION REPRESENTATION LEARNING

CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training

ICLR 2018 mkocaoglu/CausalGAN

We show that adversarial training can be used to learn a generative model with true observational and interventional distributions if the generator architecture is consistent with the given causal graph. The conditional GAN combined with a trained causal implicit generative model for the labels is then a causal implicit generative model over the labels and the generated image.

FACE GENERATION

Semi-supervised Adversarial Learning to Generate Photorealistic Face Images of New Identities from 3D Morphable Model

ECCV 2018 barisgecer/facegan

We propose a novel end-to-end semi-supervised adversarial framework to generate photorealistic face images of new identities with wide ranges of expressions, poses, and illuminations conditioned by a 3D morphable model. Previous adversarial style-transfer methods either supervise their networks with large volume of paired data or use unpaired data with a highly under-constrained two-way generative framework in an unsupervised fashion.

DOMAIN ADAPTATION FACE GENERATION FACE RECOGNITION STYLE TRANSFER

Triple consistency loss for pairing distributions in GAN-based face synthesis

8 Nov 2018ESanchezLozano/GANnotation

In this paper, we show empirical evidence of this effect, and propose a new loss to bridge the gap between the distributions of the input and target domains. To show this is effective, we incorporate the triple consistency loss into the training of a new landmark-guided face to face synthesis, where, contrary to previous works, the generated images can simultaneously undergo a large transformation in both expression and pose.

FACE GENERATION

Everybody Dance Now

22 Aug 2018Lotayou/everybody_dance_now_pytorch

This paper presents a simple method for "do as I do" motion transfer: given a source video of a person dancing we can transfer that performance to a novel (amateur) target after only a few minutes of the target subject performing standard moves. We pose this problem as a per-frame image-to-image translation with spatio-temporal smoothing.

FACE GENERATION IMAGE-TO-IMAGE TRANSLATION VIDEO GENERATION

Face Synthesis from Visual Attributes via Sketch using Conditional VAEs and GANs

30 Dec 2017DetionDX/Attribute2Sketch2Face

In this paper, we take a different approach, where we formulate the original problem as a stage-wise learning problem. We first synthesize the facial sketch corresponding to the visual attributes and then we reconstruct the face image based on the synthesized sketch.

FACE GENERATION

GP-GAN: Gender Preserving GAN for Synthesizing Faces from Landmarks

3 Oct 2017DetionDX/GP-GAN-GenderPreserving-GAN-for-Synthesizing-Faces-from-Landmarks

Several works exist that attempt to perform high-level face-related analysis tasks based on landmarks. The primary aim of this work is to demonstrate that information preserved by landmarks (gender in particular) can be further accentuated by leveraging generative models to synthesize corresponding faces.

FACE GENERATION

RankGAN: A Maximum Margin Ranking GAN for Generating Faces

19 Dec 2018human-analysis/RankGAN

We present a new stage-wise learning paradigm for training generative adversarial networks (GANs). The goal of our work is to progressively strengthen the discriminator and thus, the generators, with each subsequent stage without changing the network architecture.

FACE GENERATION