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Found 12 papers, 9 papers with code
Facial Geometric Detail Recovery via Implicit Representation
Learning a dense 3D model with fine-scale details from a single facial image is highly challenging and ill-posed.
AvatarMe++: Facial Shape and BRDF Inference with Photorealistic Rendering-Aware GANs
Nevertheless, to the best of our knowledge, there is no method which can produce render-ready high-resolution 3D faces from "in-the-wild" images and this can be attributed to the: (a) scarcity of available data for training, and (b) lack of robust methodologies that can successfully be applied on very high-resolution data.
Fast-GANFIT: Generative Adversarial Network for High Fidelity 3D Face Reconstruction
In this paper, we take a radically different approach and harness the power of Generative Adversarial Networks (GANs) and DCNNs in order to reconstruct the facial texture and shape from single images.
Robust Egocentric Photo-realistic Facial Expression Transfer for Virtual Reality
Social presence, the feeling of being there with a real person, will fuel the next generation of communication systems driven by digital humans in virtual reality (VR).
OSTeC: One-Shot Texture Completion
Many recent 3D facial texture reconstruction and pose manipulation from a single image approaches still rely on large and clean face datasets to train image-to-image Generative Adversarial Networks (GANs).
AvatarMe: Realistically Renderable 3D Facial Reconstruction "in-the-wild"
Over the last years, with the advent of Generative Adversarial Networks (GANs), many face analysis tasks have accomplished astounding performance, with applications including, but not limited to, face generation and 3D face reconstruction from a single "in-the-wild" image.
Towards a complete 3D morphable model of the human head
Eye and eye region models are incorporated into the head model, along with basic models of the teeth, tongue and inner mouth cavity.
Synthesizing Coupled 3D Face Modalities by Trunk-Branch Generative Adversarial Networks
In this paper, we present the first methodology that generates high-quality texture, shape, and normals jointly, which can be used for photo-realistic synthesis.
GANFIT: Generative Adversarial Network Fitting for High Fidelity 3D Face Reconstruction
In this paper, we take a radically different approach and harness the power of Generative Adversarial Networks (GANs) and DCNNs in order to reconstruct the facial texture and shape from single images.
Ranked #1 on
3D Face Reconstruction
on Florence
(Average 3D Error metric)
Semi-supervised Adversarial Learning to Generate Photorealistic Face Images of New Identities from 3D Morphable Model
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.
Ranked #16 on
Face Verification
on IJB-A
Learning Deep Convolutional Embeddings for Face Representation Using Joint Sample- and Set-based Supervision
In this work, we investigate several methods and strategies to learn deep embeddings for face recognition, using joint sample- and set-based optimization.
Evaluation of Joint Multi-Instance Multi-Label Learning For Breast Cancer Diagnosis
Multi-instance multi-label (MIML) learning is a challenging problem in many aspects.
