9,046 research outputs found
Joint Material and Illumination Estimation from Photo Sets in the Wild
Faithful manipulation of shape, material, and illumination in 2D Internet
images would greatly benefit from a reliable factorization of appearance into
material (i.e., diffuse and specular) and illumination (i.e., environment
maps). On the one hand, current methods that produce very high fidelity
results, typically require controlled settings, expensive devices, or
significant manual effort. To the other hand, methods that are automatic and
work on 'in the wild' Internet images, often extract only low-frequency
lighting or diffuse materials. In this work, we propose to make use of a set of
photographs in order to jointly estimate the non-diffuse materials and sharp
lighting in an uncontrolled setting. Our key observation is that seeing
multiple instances of the same material under different illumination (i.e.,
environment), and different materials under the same illumination provide
valuable constraints that can be exploited to yield a high-quality solution
(i.e., specular materials and environment illumination) for all the observed
materials and environments. Similar constraints also arise when observing
multiple materials in a single environment, or a single material across
multiple environments. The core of this approach is an optimization procedure
that uses two neural networks that are trained on synthetic images to predict
good gradients in parametric space given observation of reflected light. We
evaluate our method on a range of synthetic and real examples to generate
high-quality estimates, qualitatively compare our results against
state-of-the-art alternatives via a user study, and demonstrate
photo-consistent image manipulation that is otherwise very challenging to
achieve
A framework for digital sunken relief generation based on 3D geometric models
Sunken relief is a special art form of sculpture whereby the depicted shapes are sunk into a given surface. This is traditionally created by laboriously carving materials such as stone. Sunken reliefs often utilize the engraved lines or strokes to strengthen the impressions of a 3D presence and to highlight the features which otherwise are unrevealed. In other types of reliefs, smooth surfaces and their shadows convey such information in a coherent manner. Existing methods for relief generation are focused on forming a smooth surface with a shallow depth which provides the presence of 3D figures. Such methods unfortunately do not help the art form of sunken reliefs as they omit the presence of feature lines. We propose a framework to produce sunken reliefs from a known 3D geometry, which transforms the 3D objects into three layers of input to incorporate the contour lines seamlessly with the smooth surfaces. The three input layers take the advantages of the geometric information and the visual cues to assist the relief generation. This framework alters existing techniques in line drawings and relief generation, and then combines them organically for this particular purpose
Editing faces in videos
Editing faces in movies is of interest in the special effects industry. We aim at
producing effects such as the addition of accessories interacting correctly with
the face or replacing the face of a stuntman with the face of the main actor.
The system introduced in this thesis is based on a 3D generative face model.
Using a 3D model makes it possible to edit the face in the semantic space of pose,
expression, and identity instead of pixel space, and due to its 3D nature allows
a modelling of the light interaction. In our system we first reconstruct the 3D
face, which is deforming because of expressions and speech, the lighting, and
the camera in all frames of a monocular input video. The face is then edited by
substituting expressions or identities with those of another video sequence or by
adding virtual objects into the scene. The manipulated 3D scene is rendered back
into the original video, correctly simulating the interaction of the light with the
deformed face and virtual objects.
We describe all steps necessary to build and apply the system. This includes
registration of training faces to learn a generative face model, semi-automatic
annotation of the input video, fitting of the face model to the input video, editing
of the fit, and rendering of the resulting scene.
While describing the application we introduce a host of new methods, each
of which is of interest on its own. We start with a new method to register 3D
face scans to use as training data for the face model. For video preprocessing a
new interest point tracking and 2D Active Appearance Model fitting technique
is proposed. For robust fitting we introduce background modelling, model-based
stereo techniques, and a more accurate light model
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