71,007 research outputs found
Photo-Realistic Real-time Face Rendering
Face rendering is a really important topic in Computer Graphics because a lot of virtual simulations or video games contain virtual humans. In order to obtain a realistic face, we need to take care of skin rendering. Nowadays, we can use modern 3D scanning technology to obtain very detailed meshes and textures for the face but the main difficulty with skin rendering is that we need to model subsurface scattering effects. In 2007, d'Eon, Eugene, David Luebke, and Eric Enderton published the article "Efficient Rendering of Human Skin" that describe an algorithm for rendering realistic skin in real-time. The goal of my work was to implement their algorithm to simulate subsurface scattering in skin. I also implemented diffuse environment lighting with occlusions using spherical harmonics
A survey of real-time crowd rendering
In this survey we review, classify and compare existing approaches for real-time crowd rendering. We first overview character animation techniques, as they are highly tied to crowd rendering performance, and then we analyze the state of the art in crowd rendering. We discuss different representations for level-of-detail (LoD) rendering of animated characters, including polygon-based, point-based, and image-based techniques, and review different criteria for runtime LoD selection. Besides LoD approaches, we review classic acceleration schemes, such as frustum culling and occlusion culling, and describe how they can be adapted to handle crowds of animated characters. We also discuss specific acceleration techniques for crowd rendering, such as primitive pseudo-instancing, palette skinning, and dynamic key-pose caching, which benefit from current graphics hardware. We also address other factors affecting performance and realism of crowds such as lighting, shadowing, clothing and variability. Finally we provide an exhaustive comparison of the most relevant approaches in the field.Peer ReviewedPostprint (author's final draft
EgoFace: Egocentric Face Performance Capture and Videorealistic Reenactment
Face performance capture and reenactment techniques use multiple cameras and sensors, positioned at a distance from the face or mounted on heavy wearable devices. This limits their applications in mobile and outdoor environments. We present EgoFace, a radically new lightweight setup for face performance capture and front-view videorealistic reenactment using a single egocentric RGB camera. Our lightweight setup allows operations in uncontrolled environments, and lends itself to telepresence applications such as video-conferencing from dynamic environments. The input image is projected into a low dimensional latent space of the facial expression parameters. Through careful adversarial training of the parameter-space synthetic rendering, a videorealistic animation is produced. Our problem is challenging as the human visual system is sensitive to the smallest face irregularities that could occur in the final results. This sensitivity is even stronger for video results. Our solution is trained in a pre-processing stage, through a supervised manner without manual annotations. EgoFace captures a wide variety of facial expressions, including mouth movements and asymmetrical expressions. It works under varying illuminations, background, movements, handles people from different ethnicities and can operate in real time
Single-shot layered reflectance separation using a polarized light field camera
We present a novel computational photography technique for single shot separation of diffuse/specular reflectance as well as novel angular domain separation of layered reflectance. Our solution consists of a two-way polarized light field (TPLF) camera which simultaneously captures two orthogonal states of polarization. A single photograph of a subject acquired with the TPLF camera under polarized illumination then enables standard separation of diffuse (depolarizing) and polarization preserving specular reflectance using light field sampling. We further demonstrate that the acquired data also enables novel angular separation of layered reflectance including separation of specular reflectance and single scattering in the polarization preserving component, and separation of shallow scattering from deep scattering in the depolarizing component. We apply our approach for efficient acquisition of facial reflectance including diffuse and specular normal maps, and novel separation of photometric normals into layered reflectance normals for layered facial renderings. We demonstrate our proposed single shot layered reflectance separation to be comparable to an existing multi-shot technique that relies on structured lighting while achieving separation results under a variety of illumination conditions
Self-supervised Multi-level Face Model Learning for Monocular Reconstruction at over 250 Hz
The reconstruction of dense 3D models of face geometry and appearance from a
single image is highly challenging and ill-posed. To constrain the problem,
many approaches rely on strong priors, such as parametric face models learned
from limited 3D scan data. However, prior models restrict generalization of the
true diversity in facial geometry, skin reflectance and illumination. To
alleviate this problem, we present the first approach that jointly learns 1) a
regressor for face shape, expression, reflectance and illumination on the basis
of 2) a concurrently learned parametric face model. Our multi-level face model
combines the advantage of 3D Morphable Models for regularization with the
out-of-space generalization of a learned corrective space. We train end-to-end
on in-the-wild images without dense annotations by fusing a convolutional
encoder with a differentiable expert-designed renderer and a self-supervised
training loss, both defined at multiple detail levels. Our approach compares
favorably to the state-of-the-art in terms of reconstruction quality, better
generalizes to real world faces, and runs at over 250 Hz.Comment: CVPR 2018 (Oral). Project webpage:
https://gvv.mpi-inf.mpg.de/projects/FML
Multi-Resolution Texture Coding for Multi-Resolution 3D Meshes
We present an innovative system to encode and transmit textured multi-resolution 3D meshes in a progressive way, with no need to send several texture images, one for each mesh LOD (Level Of Detail). All texture LODs are created from the finest one (associated to the finest mesh), but can be re- constructed progressively from the coarsest thanks to refinement images calculated in the encoding process, and transmitted only if needed. This allows us to adjust the LOD/quality of both 3D mesh and texture according to the rendering power of the device that will display them, and to the network capacity. Additionally, we achieve big savings in data transmission by avoiding altogether texture coordinates, which are generated automatically thanks to an unwrapping system agreed upon by both encoder and decoder
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