Jose I. Echevarria

Capturing and stylizing hair for 3D fabrication

Recently, we have seen a growing trend in the design and fabrication of personalized figurines, created by scanning real people and then physically reproducing miniature statues with 3D printers. This is currently a hot topic both in academia and industry, and the printed figurines are gaining more and more realism, especially with state-of-the-art facial scanning technology improving. However, current systems all contain the same limitation - no previous method is able to suitably capture personalized hair-styles for physical reproduction. Typically, the subjects hair is approximated very coarsely or replaced completely with a template model. In this paper we present the first method for stylized hair capture, a technique to reconstruct an individuals actual hair-style in a manner suitable for physical reproduction. Inspired by centuries-old artistic sculptures, our method generates hair as a closed-manifold surface, yet contains the structural and color elements stylized in a way that captures the defining characteristics of the hair-style. The key to our approach is a novel multi-view stylization algorithm, which extends feature-preserving color filtering from 2D images to irregular manifolds in 3D, and introduces abstract geometric details that are coherent with the color stylization. The proposed technique fits naturally in traditional pipelines for figurine reproduction, and we demonstrate the robustness and versatility of our approach by capturing several subjects with widely varying hair-styles.

SMAA: Enhanced Subpixel Morphological Antialiasing

We present a new image‐based, post‐processing antialiasing technique, which offers practical solutions to the common, open problems of existing filter‐based real‐time antialiasing algorithms. Some of the new features include local contrast analysis for more reliable edge detection, and a simple and effective way to handle sharp geometric features and diagonal lines. This, along with our accelerated and accurate pattern classification allows for a better reconstruction of silhouettes. Our method shows for the first time how to combine morphological antialiasing (MLAA) with additional multi/supersampling strategies (MSAA, SSAA) for accurate subpixel features, and how to couple it with temporal reprojection; always preserving the sharpness of the image. All these solutions combine synergies making for a very robust technique, yielding results of better overall quality than previous approaches while more closely converging to MSAA/SSAA references but maintaining extremely fast execution times. Additionally, we propose different presets to better fit the available resources or particular needs of each scenario.

BSSRDF Estimation from Single Images

We present a novel method to estimate an approximation of the reflectance characteristics of optically thick, homogeneous translucent materials using only a single photograph as input. First, we approximate the diffusion profile as a linear combination of piecewise constant functions, an approach that enables a linear system minimization and maximizes robustness in the presence of suboptimal input data inferred from the image. We then fit to a smoother monotonically decreasing model, ensuring continuity on its first derivative. We show the feasibility of our approach and validate it in controlled environments, comparing well against physical measurements from previous works. Next, we explore the performance of our method in uncontrolled scenarios, where neither lighting nor geometry are known. We show that these can be roughly approximated from the corresponding image by making two simple assumptions: that the object is lit by a distant light source and that it is globally convex, allowing us to capture the visual appearance of the photographed material. Compared with previous works, our technique offers an attractive balance between visual accuracy and ease of use, allowing its use in a wide range of scenarios including off‐the‐shelf, single images, thus extending the current repertoire of real‐world data acquisition techniques.

Fast depth from defocus from focal stacks

We present a new depth from defocus method based on the assumption that a per pixel blur estimate (related with the circle of confusion), while ambiguous for a single image, behaves in a consistent way when applied over a focal stack of two or more images. This allows us to fit a simple analytical description of the circle of confusion to the different per pixel measures to obtain approximate depth values up to a scale. Our results are comparable to previous work while offering a faster and flexible pipeline.

Computational simulation of alternative photographic processes

We present a novel computational framework for physically and chemically‐based simulations of analog alternative photographic processes. In the real world, these processes allow the creation of very personal and unique depictions due to the combination of the chemicals used, the physical interaction with liquid solutions, and the individual craftsmanship of the artist. Our work focuses not only on achieving similar compelling results, but on the manual process as well, introducing a novel exploratory approach for interactive digital image creation and manipulation. With such an emphasis on the user interaction, our simulations are devised to run on tablet devices; thus we propose the combination of a lightweight data‐driven model to simulate the chemical reactions involved, with efficient fluids simulations that modulate them. This combination allows realistic gestures‐based user interaction with constant visual feedback in real‐time. Using the proposed framework, we have built two prototypes with different tradeoffs between realism and flexibility, showing its potential to build novel image editing tools.

Convolution-Based Simulation of Homogeneous Subsurface Scattering

This paper introduces a new method for simulating homogeneous subsurface light transport in translucent objects. Our approach is based on irradiance convolutions over a multi‐layered representation of the volume for light transport, which is general enough to obtain plausible depictions of translucent objects based on the diffusion approximation. We aim at providing an efficient physically based algorithm that can apply arbitrary diffusion profiles to general geometries. We obtain accurate results for a wide range of materials, on par with the hierarchical method by Jensen and Buhler.

Intrinsic Light Field Images

We present a method to automatically decompose a light field into its intrinsic shading and albedo components. Contrary to previous work targeted to two‐dimensional (2D) single images and videos, a light field is a 4D structure that captures non‐integrated incoming radiance over a discrete angular domain. This higher dimensionality of the problem renders previous state‐of‐the‐art algorithms impractical either due to their cost of processing a single 2D slice, or their inability to enforce proper coherence in additional dimensions. We propose a new decomposition algorithm that jointly optimizes the whole light field data for proper angular coherence. For efficiency, we extend Retinex theory, working on the gradient domain, where new albedo and occlusion terms are introduced. Results show that our method provides 4D intrinsic decompositions difficult to achieve with previous state‐of‐the‐art algorithms. We further provide a comprehensive analysis and comparisons with existing intrinsic image/video decomposition methods on light field images.