3,587 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
Understanding Next-Generation VR: Classifying Commodity Clusters for Immersive Virtual Reality
Commodity clusters offer the ability to deliver higher performance computer graphics at lower prices than traditional graphics supercomputers. Immersive virtual reality systems demand notoriously high computational requirements to deliver adequate real-time graphics, leading to the emergence of commodity clusters for immersive virtual reality. Such clusters deliver the graphics power needed by leveraging the combined power of several computers to meet the demands of real-time interactive immersive computer graphics.However, the field of commodity cluster-based virtual reality is still in early stages of development and the field is currently adhoc in nature and lacks order. There is no accepted means for comparing approaches and implementers are left with instinctual or trial-and-error means for selecting an approach.This paper provides a classification system that facilitates understanding not only of the nature of different clustering systems but also the interrelations between them. The system is built from a new model for generalized computer graphics applications, which is based on the flow of data through a sequence of operations over the entire context of the application. Prior models and classification systems have been too focused in context and application whereas the system described here provides a unified means for comparison of works within the field
Procedural Modeling and Physically Based Rendering for Synthetic Data Generation in Automotive Applications
We present an overview and evaluation of a new, systematic approach for
generation of highly realistic, annotated synthetic data for training of deep
neural networks in computer vision tasks. The main contribution is a procedural
world modeling approach enabling high variability coupled with physically
accurate image synthesis, and is a departure from the hand-modeled virtual
worlds and approximate image synthesis methods used in real-time applications.
The benefits of our approach include flexible, physically accurate and scalable
image synthesis, implicit wide coverage of classes and features, and complete
data introspection for annotations, which all contribute to quality and cost
efficiency. To evaluate our approach and the efficacy of the resulting data, we
use semantic segmentation for autonomous vehicles and robotic navigation as the
main application, and we train multiple deep learning architectures using
synthetic data with and without fine tuning on organic (i.e. real-world) data.
The evaluation shows that our approach improves the neural network's
performance and that even modest implementation efforts produce
state-of-the-art results.Comment: The project web page at
http://vcl.itn.liu.se/publications/2017/TKWU17/ contains a version of the
paper with high-resolution images as well as additional materia
Looking Beyond a Clever Narrative: Visual Context and Attention are Primary Drivers of Affect in Video Advertisements
Emotion evoked by an advertisement plays a key role in influencing brand
recall and eventual consumer choices. Automatic ad affect recognition has
several useful applications. However, the use of content-based feature
representations does not give insights into how affect is modulated by aspects
such as the ad scene setting, salient object attributes and their interactions.
Neither do such approaches inform us on how humans prioritize visual
information for ad understanding. Our work addresses these lacunae by
decomposing video content into detected objects, coarse scene structure, object
statistics and actively attended objects identified via eye-gaze. We measure
the importance of each of these information channels by systematically
incorporating related information into ad affect prediction models. Contrary to
the popular notion that ad affect hinges on the narrative and the clever use of
linguistic and social cues, we find that actively attended objects and the
coarse scene structure better encode affective information as compared to
individual scene objects or conspicuous background elements.Comment: Accepted for publication in the Proceedings of 20th ACM International
Conference on Multimodal Interaction, Boulder, CO, US
Deep Reflectance Maps
Undoing the image formation process and therefore decomposing appearance into
its intrinsic properties is a challenging task due to the under-constraint
nature of this inverse problem. While significant progress has been made on
inferring shape, materials and illumination from images only, progress in an
unconstrained setting is still limited. We propose a convolutional neural
architecture to estimate reflectance maps of specular materials in natural
lighting conditions. We achieve this in an end-to-end learning formulation that
directly predicts a reflectance map from the image itself. We show how to
improve estimates by facilitating additional supervision in an indirect scheme
that first predicts surface orientation and afterwards predicts the reflectance
map by a learning-based sparse data interpolation.
In order to analyze performance on this difficult task, we propose a new
challenge of Specular MAterials on SHapes with complex IllumiNation (SMASHINg)
using both synthetic and real images. Furthermore, we show the application of
our method to a range of image-based editing tasks on real images.Comment: project page: http://homes.esat.kuleuven.be/~krematas/DRM
Text2Scene: Text-driven Indoor Scene Stylization with Part-aware Details
We propose Text2Scene, a method to automatically create realistic textures
for virtual scenes composed of multiple objects. Guided by a reference image
and text descriptions, our pipeline adds detailed texture on labeled 3D
geometries in the room such that the generated colors respect the hierarchical
structure or semantic parts that are often composed of similar materials.
Instead of applying flat stylization on the entire scene at a single step, we
obtain weak semantic cues from geometric segmentation, which are further
clarified by assigning initial colors to segmented parts. Then we add texture
details for individual objects such that their projections on image space
exhibit feature embedding aligned with the embedding of the input. The
decomposition makes the entire pipeline tractable to a moderate amount of
computation resources and memory. As our framework utilizes the existing
resources of image and text embedding, it does not require dedicated datasets
with high-quality textures designed by skillful artists. To the best of our
knowledge, it is the first practical and scalable approach that can create
detailed and realistic textures of the desired style that maintain structural
context for scenes with multiple objects.Comment: Accepted to CVPR 202
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