5,036 research outputs found
Live User-guided Intrinsic Video For Static Scenes
We present a novel real-time approach for user-guided intrinsic decomposition of static scenes captured by an RGB-D sensor. In the first step, we acquire a three-dimensional representation of the scene using a dense volumetric reconstruction framework. The obtained reconstruction serves as a proxy to densely fuse reflectance estimates and to store user-provided constraints in three-dimensional space. User constraints, in the form of constant shading and reflectance strokes, can be placed directly on the real-world geometry using an intuitive touch-based interaction metaphor, or using interactive mouse strokes. Fusing the decomposition results and constraints in three-dimensional space allows for robust propagation of this information to novel views by re-projection.We leverage this information to improve on the decomposition quality of existing intrinsic video decomposition techniques by further constraining the ill-posed decomposition problem. In addition to improved decomposition quality, we show a variety of live augmented reality applications such as recoloring of objects, relighting of scenes and editing of material appearance
Tex2Shape: Detailed Full Human Body Geometry From a Single Image
We present a simple yet effective method to infer detailed full human body shape from only a single photograph. Our model can infer full-body shape including face, hair, and clothing including wrinkles at interactive frame-rates. Results feature details even on parts that are occluded in the input image. Our main idea is to turn shape regression into an aligned image-to-image translation problem. The input to our method is a partial texture map of the visible region obtained from off-the-shelf methods. From a partial texture, we estimate detailed normal and vector displacement maps, which can be applied to a low-resolution smooth body model to add detail and clothing. Despite being trained purely with synthetic data, our model generalizes well to real-world photographs. Numerous results demonstrate the versatility and robustness of our method
Self-supervised Multi-level Face Model Learning for Monocular Reconstruction at over 250 Hz
The reconstruction of dense 3D models of face geometry and appearance from a
single image is highly challenging and ill-posed. To constrain the problem,
many approaches rely on strong priors, such as parametric face models learned
from limited 3D scan data. However, prior models restrict generalization of the
true diversity in facial geometry, skin reflectance and illumination. To
alleviate this problem, we present the first approach that jointly learns 1) a
regressor for face shape, expression, reflectance and illumination on the basis
of 2) a concurrently learned parametric face model. Our multi-level face model
combines the advantage of 3D Morphable Models for regularization with the
out-of-space generalization of a learned corrective space. We train end-to-end
on in-the-wild images without dense annotations by fusing a convolutional
encoder with a differentiable expert-designed renderer and a self-supervised
training loss, both defined at multiple detail levels. Our approach compares
favorably to the state-of-the-art in terms of reconstruction quality, better
generalizes to real world faces, and runs at over 250 Hz.Comment: CVPR 2018 (Oral). Project webpage:
https://gvv.mpi-inf.mpg.de/projects/FML
Tex2Shape: Detailed Full Human Body Geometry From a Single Image
We present a simple yet effective method to infer detailed full human body
shape from only a single photograph. Our model can infer full-body shape
including face, hair, and clothing including wrinkles at interactive
frame-rates. Results feature details even on parts that are occluded in the
input image. Our main idea is to turn shape regression into an aligned
image-to-image translation problem. The input to our method is a partial
texture map of the visible region obtained from off-the-shelf methods. From a
partial texture, we estimate detailed normal and vector displacement maps,
which can be applied to a low-resolution smooth body model to add detail and
clothing. Despite being trained purely with synthetic data, our model
generalizes well to real-world photographs. Numerous results demonstrate the
versatility and robustness of our method
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
HeadOn: Real-time Reenactment of Human Portrait Videos
We propose HeadOn, the first real-time source-to-target reenactment approach
for complete human portrait videos that enables transfer of torso and head
motion, face expression, and eye gaze. Given a short RGB-D video of the target
actor, we automatically construct a personalized geometry proxy that embeds a
parametric head, eye, and kinematic torso model. A novel real-time reenactment
algorithm employs this proxy to photo-realistically map the captured motion
from the source actor to the target actor. On top of the coarse geometric
proxy, we propose a video-based rendering technique that composites the
modified target portrait video via view- and pose-dependent texturing, and
creates photo-realistic imagery of the target actor under novel torso and head
poses, facial expressions, and gaze directions. To this end, we propose a
robust tracking of the face and torso of the source actor. We extensively
evaluate our approach and show significant improvements in enabling much
greater flexibility in creating realistic reenacted output videos.Comment: Video: https://www.youtube.com/watch?v=7Dg49wv2c_g Presented at
Siggraph'1
Cross-calibration of Time-of-flight and Colour Cameras
Time-of-flight cameras provide depth information, which is complementary to
the photometric appearance of the scene in ordinary images. It is desirable to
merge the depth and colour information, in order to obtain a coherent scene
representation. However, the individual cameras will have different viewpoints,
resolutions and fields of view, which means that they must be mutually
calibrated. This paper presents a geometric framework for this multi-view and
multi-modal calibration problem. It is shown that three-dimensional projective
transformations can be used to align depth and parallax-based representations
of the scene, with or without Euclidean reconstruction. A new evaluation
procedure is also developed; this allows the reprojection error to be
decomposed into calibration and sensor-dependent components. The complete
approach is demonstrated on a network of three time-of-flight and six colour
cameras. The applications of such a system, to a range of automatic
scene-interpretation problems, are discussed.Comment: 18 pages, 12 figures, 3 table
Learning to Reconstruct People in Clothing from a Single RGB Camera
We present a learning-based model to infer the personalized 3D shape of people from a few frames (1-8) of a monocular video in which the person is moving, in less than 10 seconds with a reconstruction accuracy of 5mm. Our model learns to predict the parameters of a statistical body model and instance displacements that add clothing and hair to the shape. The model achieves fast and accurate predictions based on two key design choices. First, by predicting shape in a canonical T-pose space, the network learns to encode the images of the person into pose-invariant latent codes, where the information is fused. Second, based on the observation that feed-forward predictions are fast but do not always align with the input images, we predict using both, bottom-up and top-down streams (one per view) allowing information to flow in both directions. Learning relies only on synthetic 3D data. Once learned, the model can take a variable number of frames as input, and is able to reconstruct shapes even from a single image with an accuracy of 6mm. Results on 3 different datasets demonstrate the efficacy and accuracy of our approach
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