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Found 13 papers, 7 papers with code
AnimateMe: 4D Facial Expressions via Diffusion Models
Recent advances in diffusion models have notably enhanced the capabilities of generative models in 2D animation.
Arc2Face: A Foundation Model of Human Faces
This paper presents Arc2Face, an identity-conditioned face foundation model, which, given the ArcFace embedding of a person, can generate diverse photo-realistic images with an unparalleled degree of face similarity than existing models.
Ranked #1 on
Diffusion Personalization Tuning Free
on AgeDB
FitDiff: Robust monocular 3D facial shape and reflectance estimation using Diffusion Models
This model accurately generates relightable facial avatars, utilizing an identity embedding extracted from an "in-the-wild" 2D facial image.
FitMe: Deep Photorealistic 3D Morphable Model Avatars
In this paper, we introduce FitMe, a facial reflectance model and a differentiable rendering optimization pipeline, that can be used to acquire high-fidelity renderable human avatars from single or multiple images.
Relightify: Relightable 3D Faces from a Single Image via Diffusion Models
Following the remarkable success of diffusion models on image generation, recent works have also demonstrated their impressive ability to address a number of inverse problems in an unsupervised way, by properly constraining the sampling process based on a conditioning input.
Physically-Based Face Rendering for NIR-VIS Face Recognition
Specifically, we reconstruct 3D face shape and reflectance from a large 2D facial dataset and introduce a novel method of transforming the VIS reflectance to NIR reflectance.
3DMM-RF: Convolutional Radiance Fields for 3D Face Modeling
In this work, we present a facial 3D Morphable Model, which exploits both of the above, and can accurately model a subject's identity, pose and expression and render it in arbitrary illumination.
Perspective Reconstruction of Human Faces by Joint Mesh and Landmark Regression
Even though 3D face reconstruction has achieved impressive progress, most orthogonal projection-based face reconstruction methods can not achieve accurate and consistent reconstruction results when the face is very close to the camera due to the distortion under the perspective projection.
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.
Variational Prototype Learning for Deep Face Recognition
Deep face recognition has achieved remarkable improvements due to the introduction of margin-based softmax loss, in which the prototype stored in the last linear layer represents the center of each class.
Sample and Computation Redistribution for Efficient Face Detection
Although tremendous strides have been made in uncontrolled face detection, efficient face detection with a low computation cost as well as high precision remains an open challenge.
Ranked #8 on
Face Detection
on WIDER Face (Medium)
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.
