796 research outputs found
Neural Face Editing with Intrinsic Image Disentangling
Traditional face editing methods often require a number of sophisticated and
task specific algorithms to be applied one after the other --- a process that
is tedious, fragile, and computationally intensive. In this paper, we propose
an end-to-end generative adversarial network that infers a face-specific
disentangled representation of intrinsic face properties, including shape (i.e.
normals), albedo, and lighting, and an alpha matte. We show that this network
can be trained on "in-the-wild" images by incorporating an in-network
physically-based image formation module and appropriate loss functions. Our
disentangling latent representation allows for semantically relevant edits,
where one aspect of facial appearance can be manipulated while keeping
orthogonal properties fixed, and we demonstrate its use for a number of facial
editing applications.Comment: CVPR 2017 ora
CNN-based Real-time Dense Face Reconstruction with Inverse-rendered Photo-realistic Face Images
With the powerfulness of convolution neural networks (CNN), CNN based face
reconstruction has recently shown promising performance in reconstructing
detailed face shape from 2D face images. The success of CNN-based methods
relies on a large number of labeled data. The state-of-the-art synthesizes such
data using a coarse morphable face model, which however has difficulty to
generate detailed photo-realistic images of faces (with wrinkles). This paper
presents a novel face data generation method. Specifically, we render a large
number of photo-realistic face images with different attributes based on
inverse rendering. Furthermore, we construct a fine-detailed face image dataset
by transferring different scales of details from one image to another. We also
construct a large number of video-type adjacent frame pairs by simulating the
distribution of real video data. With these nicely constructed datasets, we
propose a coarse-to-fine learning framework consisting of three convolutional
networks. The networks are trained for real-time detailed 3D face
reconstruction from monocular video as well as from a single image. Extensive
experimental results demonstrate that our framework can produce high-quality
reconstruction but with much less computation time compared to the
state-of-the-art. Moreover, our method is robust to pose, expression and
lighting due to the diversity of data.Comment: Accepted by IEEE Transactions on Pattern Analysis and Machine
Intelligence, 201
DiffusionRig: Learning Personalized Priors for Facial Appearance Editing
We address the problem of learning person-specific facial priors from a small
number (e.g., 20) of portrait photos of the same person. This enables us to
edit this specific person's facial appearance, such as expression and lighting,
while preserving their identity and high-frequency facial details. Key to our
approach, which we dub DiffusionRig, is a diffusion model conditioned on, or
"rigged by," crude 3D face models estimated from single in-the-wild images by
an off-the-shelf estimator. On a high level, DiffusionRig learns to map
simplistic renderings of 3D face models to realistic photos of a given person.
Specifically, DiffusionRig is trained in two stages: It first learns generic
facial priors from a large-scale face dataset and then person-specific priors
from a small portrait photo collection of the person of interest. By learning
the CGI-to-photo mapping with such personalized priors, DiffusionRig can "rig"
the lighting, facial expression, head pose, etc. of a portrait photo,
conditioned only on coarse 3D models while preserving this person's identity
and other high-frequency characteristics. Qualitative and quantitative
experiments show that DiffusionRig outperforms existing approaches in both
identity preservation and photorealism. Please see the project website:
https://diffusionrig.github.io for the supplemental material, video, code, and
data.Comment: CVPR 2023. Project website: https://diffusionrig.github.i
FaceLit: Neural 3D Relightable Faces
We propose a generative framework, FaceLit, capable of generating a 3D face
that can be rendered at various user-defined lighting conditions and views,
learned purely from 2D images in-the-wild without any manual annotation. Unlike
existing works that require careful capture setup or human labor, we rely on
off-the-shelf pose and illumination estimators. With these estimates, we
incorporate the Phong reflectance model in the neural volume rendering
framework. Our model learns to generate shape and material properties of a face
such that, when rendered according to the natural statistics of pose and
illumination, produces photorealistic face images with multiview 3D and
illumination consistency. Our method enables photorealistic generation of faces
with explicit illumination and view controls on multiple datasets - FFHQ,
MetFaces and CelebA-HQ. We show state-of-the-art photorealism among 3D aware
GANs on FFHQ dataset achieving an FID score of 3.5.Comment: CVPR 202
Retrieval and Clustering from a 3D Human Database based on Body and Head Shape
In this paper, we describe a framework for similarity based retrieval and
clustering from a 3D human database. Our technique is based on both body and
head shape representation and the retrieval is based on similarity of both of
them. The 3D human database used in our study is the CAESAR anthropometric
database which contains approximately 5000 bodies. We have developed a
web-based interface for specifying the queries to interact with the retrieval
system. Our approach performs the similarity based retrieval in a reasonable
amount of time and is a practical approach.Comment: Published in Proceedings of the 2006 Digital Human Modeling for
Design and Engineering Conference, July 2006, Lyon, FRANCE, Session: Advanced
Size/Shape Analysis Paper Number: 2006-01-2355
http://papers.sae.org/2006-01-235
Multilinear methods for disentangling variations with applications to facial analysis
Several factors contribute to the appearance of an object in a visual scene, including pose,
illumination, and deformation, among others. Each factor accounts for a source of variability
in the data. It is assumed that the multiplicative interactions of these factors emulate the
entangled variability, giving rise to the rich structure of visual object appearance. Disentangling
such unobserved factors from visual data is a challenging task, especially when the data have
been captured in uncontrolled recording conditions (also referred to as “in-the-wild”) and label
information is not available. The work presented in this thesis focuses on disentangling the
variations contained in visual data, in particular applied to 2D and 3D faces. The motivation
behind this work lies in recent developments in the field, such as (i) the creation of large, visual
databases for face analysis, with (ii) the need of extracting information without the use of labels
and (iii) the need to deploy systems under demanding, real-world conditions.
In the first part of this thesis, we present a method to synthesise plausible 3D expressions
that preserve the identity of a target subject. This method is supervised as the model uses
labels, in this case 3D facial meshes of people performing a defined set of facial expressions, to
learn. The ability to synthesise an entire facial rig from a single neutral expression has a large
range of applications both in computer graphics and computer vision, ranging from the ecient
and cost-e↵ective creation of CG characters to scalable data generation for machine learning
purposes. Unlike previous methods based on multilinear models, the proposed approach is
capable to extrapolate well outside the sample pool, which allows it to accurately reproduce
the identity of the target subject and create artefact-free expression shapes while requiring
only a small input dataset. We introduce global-local multilinear models that leverage the
strengths of expression-specific and identity-specific local models combined with coarse motion
estimations from a global model. The expression-specific and identity-specific local models
are built from di↵erent slices of the patch-wise local multilinear model. Experimental results
show that we achieve high-quality, identity-preserving facial expression synthesis results that
outperform existing methods both quantitatively and qualitatively.
In the second part of this thesis, we investigate how the modes of variations from visual data
can be extracted. Our assumption is that visual data has an underlying structure consisting of
factors of variation and their interactions. Finding this structure and the factors is important
as it would not only help us to better understand visual data but once obtained we can edit the factors for use in various applications. Shape from Shading and expression transfer are just two
of the potential applications. To extract the factors of variation, several supervised methods
have been proposed but they require both labels regarding the modes of variations and the same
number of samples under all modes of variations. Therefore, their applicability is limited to
well-organised data, usually captured in well-controlled conditions. We propose a novel general
multilinear matrix decomposition method that discovers the multilinear structure of possibly
incomplete sets of visual data in unsupervised setting. We demonstrate the applicability of the
proposed method in several computer vision tasks, including Shape from Shading (SfS) (in the
wild and with occlusion removal), expression transfer, and estimation of surface normals from
images captured in the wild.
Finally, leveraging the unsupervised multilinear method proposed as well as recent advances in
deep learning, we propose a weakly supervised deep learning method for disentangling multiple
latent factors of variation in face images captured in-the-wild. To this end, we propose a deep
latent variable model, where we model the multiplicative interactions of multiple latent factors
of variation explicitly as a multilinear structure. We demonstrate that the proposed approach
indeed learns disentangled representations of facial expressions and pose, which can be used in
various applications, including face editing, as well as 3D face reconstruction and classification
of facial expression, identity and pose.Open Acces
Drivable 3D Gaussian Avatars
We present Drivable 3D Gaussian Avatars (D3GA), the first 3D controllable
model for human bodies rendered with Gaussian splats. Current photorealistic
drivable avatars require either accurate 3D registrations during training,
dense input images during testing, or both. The ones based on neural radiance
fields also tend to be prohibitively slow for telepresence applications. This
work uses the recently presented 3D Gaussian Splatting (3DGS) technique to
render realistic humans at real-time framerates, using dense calibrated
multi-view videos as input. To deform those primitives, we depart from the
commonly used point deformation method of linear blend skinning (LBS) and use a
classic volumetric deformation method: cage deformations. Given their smaller
size, we drive these deformations with joint angles and keypoints, which are
more suitable for communication applications. Our experiments on nine subjects
with varied body shapes, clothes, and motions obtain higher-quality results
than state-of-the-art methods when using the same training and test data.Comment: Website: https://zielon.github.io/d3ga
- …