Texture Measurement - Paper Copilot

Reflectance and Texture of Real-World Surfaces

Kristin J. Dana, Shree K. Nayar, Bram Van Ginneken, Jan J. Koenderink

Columbia University; Utrecht University

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Abstract

In this work, we investigate the visual appearance of real-world surfaces and the dependence of appearance on the geometry of imaging conditions. We discuss a new texture representation called the BTF (bidirectional texture function) which captures the variation in texture with illumination and viewing direction. We present a BTF database with image textures from over 60 different samples, each observed with over 200 different combinations of viewing and illumination directions. We describe the methods involved in collecting the database as well as the importqance and uniqueness of this database for computer graphics. A related quantity to the BTF is the familiar BRDF (bidirectional reflectance distribution function). The measurement methods involved in the BTF database are conducive to simultaneous measurement of the BRDF. Accordingly, we also present a BRDF database with reflectance measurements for over 60 different samples, each observed with over 200 different combinations of viewing and illumination directions. Both of these unique databases are publicly available and have important implications for computer graphics.

Authentic Volumetric Avatars from a Phone Scan

CHEN CAO, TOMAS SIMON, JIN KYU KIM, GABE SCHWARTZ, MICHAEL ZOLLHOEFER, SHUNSUKE SAITO, STEPHEN LOMBARDI, SHIH-EN WEI, DANIELLE BELKO, SHOOU-I YU, YASER SHEIKH,and JASON SARAGIH

Reality Labs

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Abstract

Creating photorealistic avatars of existing people currently requires extensive person-specific data capture, which is usually only accessible to the VFX industry and not the general public. Our work aims to address this drawback by relying only on a short mobile phone capture to obtain a drivable 3D head avatar that matches a person’s likeness faithfully. In contrast to existing approaches, our architecture avoids the complex task of directly modeling the entire manifold of human appearance, aiming instead to generate an avatar model that can be specialized to novel identities using only small amounts of data. The model dispenses with low-dimensional latent spaces that are commonly employed for hallucinating novel identities, and instead, uses a conditional representation that can extract person-specific information at multiple scales from a high resolution registered neutral phone scan. We achieve high quality results through the use of a novel universal avatar prior that has been trained on high resolution multi-view video captures of facial performances of hundreds of human subjects. By fine-tuning the model us ing inverse rendering we achieve increased realism and personalize its range of motion. The output of our approach is not only a high-fidelity 3D head avatar that matches the person’s facial shape and appearance, but one that can also be driven using a jointly discovered shared global expression space with disentangled controls for gaze direction. Via a series of experiments we demonstrate that our avatars are faithful representations of the subject’s likeness. Compared to other state-of-the-art methods for lightweight avatar creation, our approach exhibits superior visual quality and animateability.

Related Works

Classical 3D/4D Face Reconstruction; Parametric Face Models; 2D Neural Rendering of Human Heads; 3D Neural Rendering of Human Heads; Light-weight Avatar Generation

Comparisons

stylized avatar creation, paGAN, RGB video-based avatar creation

Category: Texture Measurement

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Abstract

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