Fast Deep Matting for Portrait Animation on Mobile Phone

Image matting plays an important role in image and video editing. However, the formulation of image matting is inherently ill-posed. Traditional methods usually employ interaction to deal with the image matting problem with trimaps and strokes, and cannot run on the mobile phone in real-time. In this paper, we propose a real-time automatic deep matting approach for mobile devices. By leveraging the densely connected blocks and the dilated convolution, a light full convolutional network is designed to predict a coarse binary mask for portrait images. And a feathering block, which is edge-preserving and matting adaptive, is further developed to learn the guided filter and transform the binary mask into alpha matte. Finally, an automatic portrait animation system based on fast deep matting is built on mobile devices, which does not need any interaction and can realize real-time matting with 15 fps. The experiments show that the proposed approach achieves comparable results with the state-of-the-art matting solvers.Comment: ACM Multimedia Conference (MM) 2017 camera-read

User-assisted intrinsic images

For many computational photography applications, the lighting and materials in the scene are critical pieces of information. We seek to obtain intrinsic images, which decompose a photo into the product of an illumination component that represents lighting effects and a reflectance component that is the color of the observed material. This is an under-constrained problem and automatic methods are challenged by complex natural images. We describe a new approach that enables users to guide an optimization with simple indications such as regions of constant reflectance or illumination. Based on a simple assumption on local reflectance distributions, we derive a new propagation energy that enables a closed form solution using linear least-squares. We achieve fast performance by introducing a novel downsampling that preserves local color distributions. We demonstrate intrinsic image decomposition on a variety of images and show applications.National Science Foundation (U.S.) (NSF CAREER award 0447561)Institut national de recherche en informatique et en automatique (France) (Associate Research Team “Flexible Rendering”)Microsoft Research (New Faculty Fellowship)Alfred P. Sloan Foundation (Research Fellowship)Quanta Computer, Inc. (MIT-Quanta T Party

Um ambiente para desevonvoimento de algoritmos de amostragem e remoção de ruído

In the context of Monte Carlo rendering, although many sampling and denoising techniques have been proposed in the last few years, the case for which one should be used for a specific scene is still to be made. Moreover, developing a new technique has required selecting a particular rendering system, which makes the technique tightly coupled to the chosen renderer and limits the amount of scenes it can be tested on. In this work, we propose a renderer-agnostic framework for developing and benchmarking sampling and denoising techniques for Monte Carlo rendering. It decouples techniques from rendering systems by hiding the renderer details behind a general API. This improves productivity and allows for direct comparisons among techniques using scenes from different rendering systems. The proposed framework contains two main parts: a software development kit that helps users to develop and and test their techniques locally, and an online system that allows users to submit their techniques and have them automatically benchmarked on our servers. We demonstrate its effectiveness by using our API to instrument four rendering systems and a variety of Monte Carlo denoising techniques — including recent learning-based ones — and performing a benchmark across different rendering systems.No contexto de Monte Carlo rendering, apesar de diversas técnicas de amostragem e remoção de ruído tenham sido propostas nos últimos anos, aportar qual técnica deve ser usada para uma cena específica ainda é uma tarefa difícil. Além disso, desenvolver uma nova técnica requer escolher um renderizador em particular, o que torna a técnica dependente do renderizador escolhido e limita a quantidade de cenas disponíveis para testar a técnica. Neste trabalho, um framework para desenvolvimento e avaliação de técnicas de amostragem e remoção de ruído para Monte Carlo rendering é proposto. Ele permite desacoplar as técnicas dos renderizadores por meio de uma API genérica, promovendo a reprodutibilidade e permitindo comparações entre técnicas utilizando-se cenas de diferentes renderizadores. O sistema proposto contém duas partes principais: um kit de desenvolvimento de software que ajuda os usuários a desenvolver e testar suas técnicas localmente, e um sistema online que permite que usuários submetam técnicas para que as mesmas sejam automaticamente avaliadas no nosso servidor. Para demonstramos a efetividade do ambiante proposto, modificamos quatro renderizadores e várias técnicas de remoção de ruído — incluindo técnicas recentes baseadas em aprendizado de máquina — e efetuamos uma avaliação utilizando cenas de diferentes renderizadores

Propiedades Físico-Mecánicas de Maderas Utilizadas en la Construcción de Viviendas Indígenas del Municipio de Tuchín Departamento de Córdoba

Indigenous communities have long used the natural resources around them. Most of the traditional construction techniques used are the result of empirical knowledge and of their own and humble style coming to life through craftsmanship. The physical and mechanical properties of the different types of wood used in the construction of indigenous households in the municipality of Tuchín are analyzed in this paper taking into account their features, uses, and renown importance. To obtain the information regarding the properties, the different uses bestowed to the various tree species were studied; in order to do so, trials to defectless wood samples were performed in accordance with established rules. The properties of moisture content, anhydrous density, basic density, basic specific gravity, total and partial contractions –radial, tangential, and volumetric, and parallel and perpendicular compression stress were determined. Las comunidades indígenas por mucho tiempo han utilizado los recursos naturales que los rodean. La mayoría de las técnicas constructivas tradicionales que utilizan son resultado del conocimiento empírico con estilo propio y sencillo, utilizando mano de obra artesanal. Es necesario conocer las propiedades físico-mecánicas de los diferentes tipos de maderas utilizadas en la construcción de las viviendas indígenas en el Municipio de Tuchín teniendo en cuenta sus características, sus usos y su importancia internacional. Para la obtención de estas propiedades se investigó sobre los diversos usos dados a la madera de diversas especies forestales; se realizaron ensayos a muestras de madera libres de defectos de acuerdo con las normas establecidas. Se determinaron las propiedades de contenido de humedad, densidad anhidra, densidad básica, peso específico básico, contracciones totales y parciales -volumétricas, radiales y tangenciales, y resistencia a la compresión paralela y perpendicular

Multiple depth maps integration for 3D reconstruction using geodesic graph cuts

Depth images, in particular depth maps estimated from stereo vision, may have a substantial amount of outliers and result in inaccurate 3D modelling and reconstruction. To address this challenging issue, in this paper, a graph-cut based multiple depth maps integration approach is proposed to obtain smooth and watertight surfaces. First, confidence maps for the depth images are estimated to suppress noise, based on which reliable patches covering the object surface are determined. These patches are then exploited to estimate the path weight for 3D geodesic distance computation, where an adaptive regional term is introduced to deal with the “shorter-cuts” problem caused by the effect of the minimal surface bias. Finally, the adaptive regional term and the boundary term constructed using patches are combined in the graph-cut framework for more accurate and smoother 3D modelling. We demonstrate the superior performance of our algorithm on the well-known Middlebury multi-view database and additionally on real-world multiple depth images captured by Kinect. The experimental results have shown that our method is able to preserve the object protrusions and details while maintaining surface smoothness