991 research outputs found
Extreme 3D Face Reconstruction: Seeing Through Occlusions
Existing single view, 3D face reconstruction methods can produce beautifully
detailed 3D results, but typically only for near frontal, unobstructed
viewpoints. We describe a system designed to provide detailed 3D
reconstructions of faces viewed under extreme conditions, out of plane
rotations, and 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). We estimate a coarse 3D face shape which
acts as a foundation and then separately layer this foundation with details
represented by a bump map. We show how a deep convolutional encoder-decoder can
be used to estimate such bump maps. We further show how this approach naturally
extends to generate plausible details for occluded facial regions. We test our
approach and its components extensively, quantitatively demonstrating the
invariance of our estimated facial details. We further provide numerous
qualitative examples showing that our method produces detailed 3D face shapes
in viewing conditions where existing state of the art often break down.Comment: Accepted to CVPR'18. Previously titled: "Extreme 3D Face
Reconstruction: Looking Past Occlusions
3D Face Reconstruction by Learning from Synthetic Data
Fast and robust three-dimensional reconstruction of facial geometric
structure from a single image is a challenging task with numerous applications.
Here, we introduce a learning-based approach for reconstructing a
three-dimensional face from a single image. Recent face recovery methods rely
on accurate localization of key characteristic points. In contrast, the
proposed approach is based on a Convolutional-Neural-Network (CNN) which
extracts the face geometry directly from its image. Although such deep
architectures outperform other models in complex computer vision problems,
training them properly requires a large dataset of annotated examples. In the
case of three-dimensional faces, currently, there are no large volume data
sets, while acquiring such big-data is a tedious task. As an alternative, we
propose to generate random, yet nearly photo-realistic, facial images for which
the geometric form is known. The suggested model successfully recovers facial
shapes from real images, even for faces with extreme expressions and under
various lighting conditions.Comment: The first two authors contributed equally to this wor
Analysis of 3D Face Reconstruction
This thesis investigates the long standing problem of 3D reconstruction from a single 2D face
image. Face reconstruction from a single 2D face image is an ill posed problem involving estimation of the intrinsic and the extrinsic camera parameters, light parameters, shape parameters
and the texture parameters. The proposed approach has many potential applications in the
law enforcement, surveillance, medicine, computer games and the entertainment industries.
This problem is addressed using an analysis by synthesis framework by reconstructing a 3D
face model from identity photographs. The identity photographs are a widely used medium for
face identi cation and can be found on identity cards and passports.
The novel contribution of this thesis is a new technique for creating 3D face models from a single
2D face image. The proposed method uses the improved dense 3D correspondence obtained
using rigid and non-rigid registration techniques. The existing reconstruction methods use the
optical
ow method for establishing 3D correspondence. The resulting 3D face database is used
to create a statistical shape model.
The existing reconstruction algorithms recover shape by optimizing over all the parameters
simultaneously. The proposed algorithm simplifies the reconstruction problem by using a step
wise approach thus reducing the dimension of the parameter space and simplifying the opti-
mization problem. In the alignment step, a generic 3D face is aligned with the given 2D face
image by using anatomical landmarks. The texture is then warped onto the 3D model by using
the spatial alignment obtained previously. The 3D shape is then recovered by optimizing over
the shape parameters while matching a texture mapped model to the target image.
There are a number of advantages of this approach. Firstly, it simpli es the optimization requirements and makes the optimization more robust. Second, there is no need to accurately
recover the illumination parameters. Thirdly, there is no need for recovering the texture parameters by using a texture synthesis approach. Fourthly, quantitative analysis is used for
improving the quality of reconstruction by improving the cost function. Previous methods use
qualitative methods such as visual analysis, and face recognition rates for evaluating reconstruction accuracy.
The improvement in the performance of the cost function occurs as a result of improvement
in the feature space comprising the landmark and intensity features. Previously, the feature
space has not been evaluated with respect to reconstruction accuracy thus leading to inaccurate
assumptions about its behaviour.
The proposed approach simpli es the reconstruction problem by using only identity images,
rather than placing eff ort on overcoming the pose, illumination and expression (PIE) variations.
This makes sense, as frontal face images under standard illumination conditions are widely
available and could be utilized for accurate reconstruction. The reconstructed 3D models with
texture can then be used for overcoming the PIE variations
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