337 research outputs found
Deep Detail Enhancement for Any Garment
Creating fine garment details requires significant efforts and huge
computational resources. In contrast, a coarse shape may be easy to acquire in
many scenarios (e.g., via low-resolution physically-based simulation, linear
blend skinning driven by skeletal motion, portable scanners). In this paper, we
show how to enhance, in a data-driven manner, rich yet plausible details
starting from a coarse garment geometry. Once the parameterization of the
garment is given, we formulate the task as a style transfer problem over the
space of associated normal maps. In order to facilitate generalization across
garment types and character motions, we introduce a patch-based formulation,
that produces high-resolution details by matching a Gram matrix based style
loss, to hallucinate geometric details (i.e., wrinkle density and shape). We
extensively evaluate our method on a variety of production scenarios and show
that our method is simple, light-weight, efficient, and generalizes across
underlying garment types, sewing patterns, and body motion.Comment: 12 page
Spatially Adaptive Cloth Regression with Implicit Neural Representations
The accurate representation of fine-detailed cloth wrinkles poses significant
challenges in computer graphics. The inherently non-uniform structure of cloth
wrinkles mandates the employment of intricate discretization strategies, which
are frequently characterized by high computational demands and complex
methodologies. Addressing this, the research introduced in this paper
elucidates a novel anisotropic cloth regression technique that capitalizes on
the potential of implicit neural representations of surfaces. Our first core
contribution is an innovative mesh-free sampling approach, crafted to reduce
the reliance on traditional mesh structures, thereby offering greater
flexibility and accuracy in capturing fine cloth details. Our second
contribution is a novel adversarial training scheme, which is designed
meticulously to strike a harmonious balance between the sampling and simulation
objectives. The adversarial approach ensures that the wrinkles are represented
with high fidelity, while also maintaining computational efficiency. Our
results showcase through various cloth-object interaction scenarios that our
method, given the same memory constraints, consistently surpasses traditional
discrete representations, particularly when modelling highly-detailed localized
wrinkles.Comment: 16 pages, 13 figure
Robotic system for garment perception and manipulation
Mención Internacional en el título de doctorGarments are a key element of people’s daily lives, as many
domestic tasks -such as laundry-, revolve around them. Performing
such tasks, generally dull and repetitive, implies devoting
many hours of unpaid labor to them, that could be freed
through automation. But automation of such tasks has been traditionally
hard due to the deformable nature of garments, that
creates additional challenges to the already existing when performing
object perception and manipulation. This thesis presents
a Robotic System for Garment Perception and Manipulation
that intends to address these challenges.
The laundry pipeline as defined in this work is composed
by four independent -but sequential- tasks: hanging, unfolding,
ironing and folding. The aim of this work is the automation of
this pipeline through a robotic system able to work on domestic
environments as a robot household companion.
Laundry starts by washing the garments, that then need to
be dried, frequently by hanging them. As hanging is a complex
task requiring bimanipulation skills and dexterity, a simplified
approach is followed in this work as a starting point, by using
a deep convolutional neural network and a custom synthetic
dataset to study if a robot can predict whether a garment will
hang or not when dropped over a hanger, as a first step towards
a more complex controller.
After the garment is dry, it has to be unfolded to ease recognition
of its garment category for the next steps. The presented
model-less unfolding method uses only color and depth information
from the garment to determine the grasp and release
points of an unfolding action, that is repeated iteratively until
the garment is fully spread.
Before storage, wrinkles have to be removed from the garment.
For that purpose, a novel ironing method is proposed,
that uses a custom wrinkle descriptor to locate the most prominent
wrinkles and generate a suitable ironing plan. The method
does not require a precise control of the light conditions of
the scene, and is able to iron using unmodified ironing tools
through a force-feedback-based controller.
Finally, the last step is to fold the garment to store it. One
key aspect when folding is to perform the folding operation in a precise manner, as errors will accumulate when several
folds are required. A neural folding controller is proposed that
uses visual feedback of the current garment shape, extracted
through a deep neural network trained with synthetic data, to
accurately perform a fold.
All the methods presented to solve each of the laundry pipeline
tasks have been validated experimentally on different robotic
platforms, including a full-body humanoid robot.La ropa es un elemento clave en la vida diaria de las personas,
no sólo a la hora de vestir, sino debido también a que muchas
de las tareas domésticas que una persona debe realizar diariamente,
como hacer la colada, requieren interactuar con ellas.
Estas tareas, a menudo tediosas y repetitivas, obligan a invertir
una gran cantidad de horas de trabajo no remunerado en
su realización, las cuales podrían reducirse a través de su automatización.
Sin embargo, automatizar dichas tareas ha sido
tradicionalmente un reto, debido a la naturaleza deformable de
las prendas, que supone una dificultad añadida a las ya existentes
al llevar a cabo percepción y manipulación de objetos a
través de robots. Esta tesis presenta un sistema robótico orientado
a la percepción y manipulación de prendas, que pretende
resolver dichos retos.
La colada es una tarea doméstica compuesta de varias subtareas
que se llevan a cabo de manera secuencial. En este trabajo,
se definen dichas subtareas como: tender, desdoblar, planchar
y doblar. El objetivo de este trabajo es automatizar estas tareas
a través de un sistema robótico capaz de trabajar en entornos
domésticos, convirtiéndose en un asistente robótico doméstico.
La colada comienza lavando las prendas, las cuales han de
ser posteriormente secadas, generalmente tendiéndolas al aire
libre, para poder realizar el resto de subtareas con ellas. Tender
la ropa es una tarea compleja, que requiere de bimanipulación
y una gran destreza al manipular la prenda. Por ello, en este
trabajo se ha optado por abordar una versión simplicada de
la tarea de tendido, como punto de partida para llevar a cabo
investigaciones más avanzadas en el futuro. A través de una red
neuronal convolucional profunda y un conjunto de datos de
entrenamiento sintéticos, se ha llevado a cabo un estudio sobre
la capacidad de predecir el resultado de dejar caer una prenda
sobre un tendedero por parte de un robot. Este estudio, que
sirve como primer paso hacia un controlador más avanzado,
ha resultado en un modelo capaz de predecir si la prenda se
quedará tendida o no a partir de una imagen de profundidad
de la misma en la posición en la que se dejará caer.
Una vez las prendas están secas, y para facilitar su reconocimiento
por parte del robot de cara a realizar las siguientes tareas, la prenda debe ser desdoblada. El método propuesto en
este trabajo para realizar el desdoble no requiere de un modelo
previo de la prenda, y utiliza únicamente información de profundidad
y color, obtenida mediante un sensor RGB-D, para
calcular los puntos de agarre y soltado de una acción de desdoble.
Este proceso es iterativo, y se repite hasta que la prenda se
encuentra totalmente desdoblada.
Antes de almacenar la prenda, se deben eliminar las posibles
arrugas que hayan surgido en el proceso de lavado y secado.
Para ello, se propone un nuevo algoritmo de planchado, que
utiliza un descriptor de arrugas desarrollado en este trabajo para
localizar las arrugas más prominentes y generar un plan de
planchado acorde a las condiciones de la prenda. A diferencia
de otros métodos existentes, este método puede aplicarse en un
entorno doméstico, ya que no requiere de un contol preciso de
las condiciones de iluminación. Además, es capaz de usar las
mismas herramientas de planchado que usaría una persona sin
necesidad de realizar modificaciones a las mismas, a través de
un controlador que usa realimentación de fuerza para aplicar
una presión constante durante el planchado.
El último paso al hacer la colada es doblar la prenda para
almacenarla. Un aspecto importante al doblar prendas es ejecutar
cada uno de los dobleces necesarios con precisión, ya que
cada error o desfase cometido en un doblez se acumula cuando
la secuencia de doblado está formada por varios dobleces
consecutivos. Para llevar a cabo estos dobleces con la precisión
requerida, se propone un controlador basado en una red neuronal,
que utiliza realimentación visual de la forma de la prenda
durante cada operación de doblado. Esta realimentación es obtenida
a través de una red neuronal profunda entrenada con
un conjunto de entrenamiento sintético, que permite estimar
la forma en 3D de la parte a doblar a través de una imagen
monocular de la misma.
Todos los métodos descritos en esta tesis han sido validados
experimentalmente con éxito en diversas plataformas robóticas,
incluyendo un robot humanoide.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Abderrahmane Kheddar.- Secretario: Ramón Ignacio Barber Castaño.- Vocal: Karinne Ramírez-Amar
Enhancing Mesh Deformation Realism: Dynamic Mesostructure Detailing and Procedural Microstructure Synthesis
Propomos uma solução para gerar dados de mapas de relevo dinâmicos para simular deformações em superfícies macias, com foco na pele humana. A solução incorpora a simulação de rugas ao nível mesoestrutural e utiliza texturas procedurais para adicionar detalhes de microestrutura estáticos. Oferece flexibilidade além da pele humana, permitindo a geração de padrões que imitam deformações em outros materiais macios, como couro, durante a animação.
As soluções existentes para simular rugas e pistas de deformação frequentemente dependem de hardware especializado, que é dispendioso e de difícil acesso. Além disso, depender exclusivamente de dados capturados limita a direção artística e dificulta a adaptação a mudanças. Em contraste, a solução proposta permite a síntese dinâmica de texturas que se adaptam às deformações subjacentes da malha de forma fisicamente plausível.
Vários métodos foram explorados para sintetizar rugas diretamente na geometria, mas sofrem de limitações como auto-interseções e maiores requisitos de armazenamento. A intervenção manual de artistas na criação de mapas de rugas e mapas de tensão permite controle, mas pode ser limitada em deformações complexas ou onde maior realismo seja necessário.
O nosso trabalho destaca o potencial dos métodos procedimentais para aprimorar a geração de padrões de deformação dinâmica, incluindo rugas, com maior controle criativo e sem depender de dados capturados. A incorporação de padrões procedimentais estáticos melhora o realismo, e a abordagem pode ser estendida além da pele para outros materiais macios.We propose a solution for generating dynamic heightmap data to simulate deformations for soft surfaces, with a focus on human skin. The solution incorporates mesostructure-level wrinkles and utilizes procedural textures to add static microstructure details. It offers flexibility beyond human skin, enabling the generation of patterns mimicking deformations in other soft materials, such as leater, during animation.
Existing solutions for simulating wrinkles and deformation cues often rely on specialized hardware, which is costly and not easily accessible. Moreover, relying solely on captured data limits artistic direction and hinders adaptability to changes. In contrast, our proposed solution provides dynamic texture synthesis that adapts to underlying mesh deformations.
Various methods have been explored to synthesize wrinkles directly to the geometry, but they suffer from limitations such as self-intersections and increased storage requirements. Manual intervention by artists using wrinkle maps and tension maps provides control but may be limited to the physics-based simulations.
Our research presents the potential of procedural methods to enhance the generation of dynamic deformation patterns, including wrinkles, with greater creative control and without reliance on captured data. Incorporating static procedural patterns improves realism, and the approach can be extended to other soft-materials beyond skin
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