3,385 research outputs found
A framework for real-time physically-based hair rendering
Hair rendering has been a major challenge in computer graphics for several years due to the complex light interactions involved. Complexity mainly stems from two aspects: the number of hair strands, and the resulting complexity of their interaction with light. In general, theoretical approaches towards a realistic hair visualization aim to develop a proper scattering model on a per-strand level, which can be extended in practice to the whole hair volume with ray tracing even though it is usually expensive in computational terms. Aiming at achieving real-time hair rendering, I analyze each component contributing to it from both theoretical and practical points of view in this work. Most approaches, both real- and non-real-time build on top of the Marschner scattering model, such as recent efficient state-of-the-art techniques introduced in Unreal Engine and Frostbite, among others. Interactive applications cannot afford the complexity of ray tracing, and they target efficiency by explicitly dealing with each component involved in both single-strand and inter-strand light interactions, applying the necessary simplifications to match the time budget. I have further implemented a framework, separating the different components, which combines aspects of these approaches towards the best possible quality and performance. The implementation achieves real-time good-looking hair, and its flexibility has allowed to perform experiments on performance, scalability, and contribution to quality of the different components
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
Photo-Realistic Rendering of Fiber Assemblies
In this thesis we introduce a novel uniform formalism for light scattering from filaments, the Bidirectional Fiber Scattering Distribution Function (BFSDF). Similar to the role of the Bidirectional Surface Scattering Reflectance Distribution Function (BSSRDF) for surfaces, the BFSDF can be seen as a general approach for describing light scattering from filaments. Based on this theoretical foundation, approximations for various levels of abstraction are derived allowing for efficient and accurate rendering of fiber assemblies, such as hair or fur. In this context novel rendering techniques accounting for all prominent effects of local and global illumination are presented. Moreover, physically-based analytical BFSDF models for human hair and other kinds of fibers are derived. Finally, using the model for human hair we make a first step towards image-based BFSDF reconstruction, where optical properties of a single strand are estimated from "synthetic photographs" (renderings) a full hairstyle
Relightable Neural Assets
High-fidelity 3D assets with materials composed of fibers (including hair),
complex layered material shaders, or fine scattering geometry are ubiquitous in
high-end realistic rendering applications. Rendering such models is
computationally expensive due to heavy shaders and long scattering paths.
Moreover, implementing the shading and scattering models is non-trivial and has
to be done not only in the 3D content authoring software (which is necessarily
complex), but also in all downstream rendering solutions. For example, web and
mobile viewers for complex 3D assets are desirable, but frequently cannot
support the full shading complexity allowed by the authoring application. Our
goal is to design a neural representation for 3D assets with complex shading
that supports full relightability and full integration into existing renderers.
We provide an end-to-end shading solution at the first intersection of a ray
with the underlying geometry. All shading and scattering is precomputed and
included in the neural asset; no multiple scattering paths need to be traced,
and no complex shading models need to be implemented to render our assets,
beyond a single neural architecture. We combine an MLP decoder with a feature
grid. Shading consists of querying a feature vector, followed by an MLP
evaluation producing the final reflectance value. Our method provides
high-fidelity shading, close to the ground-truth Monte Carlo estimate even at
close-up views. We believe our neural assets could be used in practical
renderers, providing significant speed-ups and simplifying renderer
implementations
Interactive translucent volume rendering and procedural modeling
Journal ArticleDirect volume rendering is a commonly used technique in visualization applications. Many of these applications require sophisticated shading models to capture subtle lighting effects and characteristics of volume metric data and materials. Many common objects and natural phenomena exhibit visual quality that cannot be captured using simple lighting models or cannot be solved at interactive rates using more sophisticated methods. We present a simple yet effective interactive shading model which captures volumetric light attenuation effects to produce volumetric shadows and the subtle appearance of translucency. We also present a technique for volume displacement or perturbation that allows realistic interactive modeling of high frequency detail for real and synthetic volumetric data
AirCode: Unobtrusive Physical Tags for Digital Fabrication
We present AirCode, a technique that allows the user to tag physically
fabricated objects with given information. An AirCode tag consists of a group
of carefully designed air pockets placed beneath the object surface. These air
pockets are easily produced during the fabrication process of the object,
without any additional material or postprocessing. Meanwhile, the air pockets
affect only the scattering light transport under the surface, and thus are hard
to notice to our naked eyes. But, by using a computational imaging method, the
tags become detectable. We present a tool that automates the design of air
pockets for the user to encode information. AirCode system also allows the user
to retrieve the information from captured images via a robust decoding
algorithm. We demonstrate our tagging technique with applications for metadata
embedding, robotic grasping, as well as conveying object affordances.Comment: ACM UIST 2017 Technical Paper
A Multi-scale Yarn Appearance Model with Fiber Details
Rendering realistic cloth has always been a challenge due to its intricate
structure. Cloth is made up of fibers, plies, and yarns, and previous
curved-based models, while detailed, were computationally expensive and
inflexible for large cloth. To address this, we propose a simplified approach.
We introduce a geometric aggregation technique that reduces ray-tracing
computation by using fewer curves, focusing only on yarn curves. Our model
generates ply and fiber shapes implicitly, compensating for the lack of
explicit geometry with a novel shadowing component. We also present a shading
model that simplifies light interactions among fibers by categorizing them into
four components, accurately capturing specular and scattered light in both
forward and backward directions.
To render large cloth efficiently, we propose a multi-scale solution based on
pixel coverage. Our yarn shading model outperforms previous methods, achieving
rendering speeds 3-5 times faster with less memory in near-field views.
Additionally, our multi-scale solution offers a 20% speed boost for distant
cloth observation
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