4,656 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
MoSculp: Interactive Visualization of Shape and Time
We present a system that allows users to visualize complex human motion via
3D motion sculptures---a representation that conveys the 3D structure swept by
a human body as it moves through space. Given an input video, our system
computes the motion sculptures and provides a user interface for rendering it
in different styles, including the options to insert the sculpture back into
the original video, render it in a synthetic scene or physically print it.
To provide this end-to-end workflow, we introduce an algorithm that estimates
that human's 3D geometry over time from a set of 2D images and develop a
3D-aware image-based rendering approach that embeds the sculpture back into the
scene. By automating the process, our system takes motion sculpture creation
out of the realm of professional artists, and makes it applicable to a wide
range of existing video material.
By providing viewers with 3D information, motion sculptures reveal space-time
motion information that is difficult to perceive with the naked eye, and allow
viewers to interpret how different parts of the object interact over time. We
validate the effectiveness of this approach with user studies, finding that our
motion sculpture visualizations are significantly more informative about motion
than existing stroboscopic and space-time visualization methods.Comment: UIST 2018. Project page: http://mosculp.csail.mit.edu
PhotoApp: Photorealistic Appearance Editing of Head Portraits
Photorealistic editing of portraits is a challenging task as humans are very
sensitive to inconsistencies in faces. We present an approach for high-quality
intuitive editing of the camera viewpoint and scene illumination in a portrait
image. This requires our method to capture and control the full reflectance
field of the person in the image. Most editing approaches rely on supervised
learning using training data captured with setups such as light and camera
stages. Such datasets are expensive to acquire, not readily available and do
not capture all the rich variations of in-the-wild portrait images. In
addition, most supervised approaches only focus on relighting, and do not allow
camera viewpoint editing. Thus, they only capture and control a subset of the
reflectance field. Recently, portrait editing has been demonstrated by
operating in the generative model space of StyleGAN. While such approaches do
not require direct supervision, there is a significant loss of quality when
compared to the supervised approaches. In this paper, we present a method which
learns from limited supervised training data. The training images only include
people in a fixed neutral expression with eyes closed, without much hair or
background variations. Each person is captured under 150 one-light-at-a-time
conditions and under 8 camera poses. Instead of training directly in the image
space, we design a supervised problem which learns transformations in the
latent space of StyleGAN. This combines the best of supervised learning and
generative adversarial modeling. We show that the StyleGAN prior allows for
generalisation to different expressions, hairstyles and backgrounds. This
produces high-quality photorealistic results for in-the-wild images and
significantly outperforms existing methods. Our approach can edit the
illumination and pose simultaneously, and runs at interactive rates.Comment: http://gvv.mpi-inf.mpg.de/projects/PhotoApp
GaussianHair: Hair Modeling and Rendering with Light-aware Gaussians
Hairstyle reflects culture and ethnicity at first glance. In the digital era,
various realistic human hairstyles are also critical to high-fidelity digital
human assets for beauty and inclusivity. Yet, realistic hair modeling and
real-time rendering for animation is a formidable challenge due to its sheer
number of strands, complicated structures of geometry, and sophisticated
interaction with light. This paper presents GaussianHair, a novel explicit hair
representation. It enables comprehensive modeling of hair geometry and
appearance from images, fostering innovative illumination effects and dynamic
animation capabilities. At the heart of GaussianHair is the novel concept of
representing each hair strand as a sequence of connected cylindrical 3D
Gaussian primitives. This approach not only retains the hair's geometric
structure and appearance but also allows for efficient rasterization onto a 2D
image plane, facilitating differentiable volumetric rendering. We further
enhance this model with the "GaussianHair Scattering Model", adept at
recreating the slender structure of hair strands and accurately capturing their
local diffuse color in uniform lighting. Through extensive experiments, we
substantiate that GaussianHair achieves breakthroughs in both geometric and
appearance fidelity, transcending the limitations encountered in
state-of-the-art methods for hair reconstruction. Beyond representation,
GaussianHair extends to support editing, relighting, and dynamic rendering of
hair, offering seamless integration with conventional CG pipeline workflows.
Complementing these advancements, we have compiled an extensive dataset of real
human hair, each with meticulously detailed strand geometry, to propel further
research in this field
PhotoApp: Photorealistic Appearance Editing of Head Portraits
Photorealistic editing of head portraits is a challenging task as humans are very sensitive to inconsistencies in faces. We present an approach for high-quality intuitive editing of the camera viewpoint and scene illumination (parameterised with an environment map) in a portrait image. This requires our method to capture and control the full reflectance field of the person in the image. Most editing approaches rely on supervised learning using training data captured with setups such as light and camera stages. Such datasets are expensive to acquire, not readily available and do not capture all the rich variations of in-the-wild portrait images. In addition, most supervised approaches only focus on relighting, and do not allow camera viewpoint editing. Thus, they only capture and control a subset of the reflectance field. Recently, portrait editing has been demonstrated by operating in the generative model space of StyleGAN. While such approaches do not require direct supervision, there is a significant loss of quality when compared to the supervised approaches. In this paper, we present a method which learns from limited supervised training data. The training images only include people in a fixed neutral expression with eyes closed, without much hair or background variations. Each person is captured under 150 one-light-at-a-time conditions and under 8 camera poses. Instead of training directly in the image space, we design a supervised problem which learns transformations in the latent space of StyleGAN. This combines the best of supervised learning and generative adversarial modeling. We show that the StyleGAN prior allows for generalisation to different expressions, hairstyles and backgrounds. This produces high-quality photorealistic results for in-the-wild images and significantly outperforms existing methods. Our approach can edit the illumination and pose simultaneously, and runs at interactive rates
Computer-assisted animation creation techniques for hair animation and shade, highlight, and shadow
制度:新 ; 報告番号:甲3062号 ; 学位の種類:博士(工学) ; 授与年月日:2010/2/25 ; 早大学位記番号:新532
State of the Art on Neural Rendering
Efficient rendering of photo-realistic virtual worlds is a long standing effort of computer graphics. Modern graphics techniques have succeeded in synthesizing photo-realistic images from hand-crafted scene representations. However, the automatic generation of shape, materials, lighting, and other aspects of scenes remains a challenging problem that, if solved, would make photo-realistic computer graphics more widely accessible. Concurrently, progress in computer vision and machine learning have given rise to a new approach to image synthesis and editing, namely deep generative models. Neural rendering is a new and rapidly emerging field that combines generative machine learning techniques with physical knowledge from computer graphics, e.g., by the integration of differentiable rendering into network training. With a plethora of applications in computer graphics and vision, neural rendering is poised to become a new area in the graphics community, yet no survey of this emerging field exists. This state-of-the-art report summarizes the recent trends and applications of neural rendering. We focus on approaches that combine classic computer graphics techniques with deep generative models to obtain controllable and photo-realistic outputs. Starting with an overview of the underlying computer graphics and machine learning concepts, we discuss critical aspects of neural rendering approaches. This state-of-the-art report is focused on the many important use cases for the described algorithms such as novel view synthesis, semantic photo manipulation, facial and body reenactment, relighting, free-viewpoint video, and the creation of photo-realistic avatars for virtual and augmented reality telepresence. Finally, we conclude with a discussion of the social implications of such technology and investigate open research problems
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