Automatic modeling of virtual humans and body clothing

Highly realistic virtual human models are rapidly becoming commonplace in computer graphics. These models, often represented by complex shape and requiring labor-intensive process, challenge the problem of automatic modeling. The problem and solutions to automatic modeling of animatable virtual humans are studied. Methods for capturing the shape of real people, parameterization techniques for modeling static shape (the variety of human body shapes) and dynamic shape (how the body shape changes as it moves) of virtual humans are classified, summarized and compared. Finally, methods for clothed virtual humans are reviewe

AI-generated Content for Various Data Modalities: A Survey

AI-generated content (AIGC) methods aim to produce text, images, videos, 3D assets, and other media using AI algorithms. Due to its wide range of applications and the demonstrated potential of recent works, AIGC developments have been attracting lots of attention recently, and AIGC methods have been developed for various data modalities, such as image, video, text, 3D shape (as voxels, point clouds, meshes, and neural implicit fields), 3D scene, 3D human avatar (body and head), 3D motion, and audio -- each presenting different characteristics and challenges. Furthermore, there have also been many significant developments in cross-modality AIGC methods, where generative methods can receive conditioning input in one modality and produce outputs in another. Examples include going from various modalities to image, video, 3D shape, 3D scene, 3D avatar (body and head), 3D motion (skeleton and avatar), and audio modalities. In this paper, we provide a comprehensive review of AIGC methods across different data modalities, including both single-modality and cross-modality methods, highlighting the various challenges, representative works, and recent technical directions in each setting. We also survey the representative datasets throughout the modalities, and present comparative results for various modalities. Moreover, we also discuss the challenges and potential future research directions

Tex2Shape: Detailed Full Human Body Geometry From a Single Image

We present a simple yet effective method to infer detailed full human body shape from only a single photograph. Our model can infer full-body shape including face, hair, and clothing including wrinkles at interactive frame-rates. Results feature details even on parts that are occluded in the input image. Our main idea is to turn shape regression into an aligned image-to-image translation problem. The input to our method is a partial texture map of the visible region obtained from off-the-shelf methods. From a partial texture, we estimate detailed normal and vector displacement maps, which can be applied to a low-resolution smooth body model to add detail and clothing. Despite being trained purely with synthetic data, our model generalizes well to real-world photographs. Numerous results demonstrate the versatility and robustness of our method

Tex2Shape: Detailed Full Human Body Geometry From a Single Image

We present a simple yet effective method to infer detailed full human body shape from only a single photograph. Our model can infer full-body shape including face, hair, and clothing including wrinkles at interactive frame-rates. Results feature details even on parts that are occluded in the input image. Our main idea is to turn shape regression into an aligned image-to-image translation problem. The input to our method is a partial texture map of the visible region obtained from off-the-shelf methods. From a partial texture, we estimate detailed normal and vector displacement maps, which can be applied to a low-resolution smooth body model to add detail and clothing. Despite being trained purely with synthetic data, our model generalizes well to real-world photographs. Numerous results demonstrate the versatility and robustness of our method

Neural Radiance Fields: Past, Present, and Future

The various aspects like modeling and interpreting 3D environments and surroundings have enticed humans to progress their research in 3D Computer Vision, Computer Graphics, and Machine Learning. An attempt made by Mildenhall et al in their paper about NeRFs (Neural Radiance Fields) led to a boom in Computer Graphics, Robotics, Computer Vision, and the possible scope of High-Resolution Low Storage Augmented Reality and Virtual Reality-based 3D models have gained traction from res with more than 1000 preprints related to NeRFs published. This paper serves as a bridge for people starting to study these fields by building on the basics of Mathematics, Geometry, Computer Vision, and Computer Graphics to the difficulties encountered in Implicit Representations at the intersection of all these disciplines. This survey provides the history of rendering, Implicit Learning, and NeRFs, the progression of research on NeRFs, and the potential applications and implications of NeRFs in today's world. In doing so, this survey categorizes all the NeRF-related research in terms of the datasets used, objective functions, applications solved, and evaluation criteria for these applications.Comment: 413 pages, 9 figures, 277 citation

HIGH QUALITY HUMAN 3D BODY MODELING, TRACKING AND APPLICATION

Geometric reconstruction of dynamic objects is a fundamental task of computer vision and graphics, and modeling human body of high fidelity is considered to be a core of this problem. Traditional human shape and motion capture techniques require an array of surrounding cameras or subjects wear reflective markers, resulting in a limitation of working space and portability. In this dissertation, a complete process is designed from geometric modeling detailed 3D human full body and capturing shape dynamics over time using a flexible setup to guiding clothes/person re-targeting with such data-driven models. As the mechanical movement of human body can be considered as an articulate motion, which is easy to guide the skin animation but has difficulties in the reverse process to find parameters from images without manual intervention, we present a novel parametric model, GMM-BlendSCAPE, jointly taking both linear skinning model and the prior art of BlendSCAPE (Blend Shape Completion and Animation for PEople) into consideration and develop a Gaussian Mixture Model (GMM) to infer both body shape and pose from incomplete observations. We show the increased accuracy of joints and skin surface estimation using our model compared to the skeleton based motion tracking. To model the detailed body, we start with capturing high-quality partial 3D scans by using a single-view commercial depth camera. Based on GMM-BlendSCAPE, we can then reconstruct multiple complete static models of large pose difference via our novel non-rigid registration algorithm. With vertex correspondences established, these models can be further converted into a personalized drivable template and used for robust pose tracking in a similar GMM framework. Moreover, we design a general purpose real-time non-rigid deformation algorithm to accelerate this registration. Last but not least, we demonstrate a novel virtual clothes try-on application based on our personalized model utilizing both image and depth cues to synthesize and re-target clothes for single-view videos of different people

Designing a Virtual Manikin Animation Framework Aimed at Virtual Prototyping

International audienceIn the industry, numerous commercial packages provide tools to introduce, and analyse human behaviour in the product's environment (for maintenance, ergonomics...), thanks to Virtual Humans. We will focus on control. Thanks to algorithms newly introduced in recent research papers, we think we can provide an implementation, which even widens, and simplifies the animation capacities of virtual manikins. In order to do so, we are going to express the industrial expectations as for Virtual Humans, without considering feasibility (not to bias the issue). The second part will show that no commercial application provides the tools that perfectly meet the needs. Thus we propose a new animation framework that better answers the problem. Our contribution is the integration - driven by need ~ of available new scientific techniques to animate Virtual Humans, in a new control scheme that better answers industrial expectations

Template based shape processing

As computers can only represent and process discrete data, information gathered from the real world always has to be sampled. While it is nowadays possible to sample many signals accurately and thus generate high-quality reconstructions (for example of images and audio data), accurately and densely sampling 3D geometry is still a challenge. The signal samples may be corrupted by noise and outliers, and contain large holes due to occlusions. These issues become even more pronounced when also considering the temporal domain. Because of this, developing methods for accurate reconstruction of shapes from a sparse set of discrete data is an important aspect of the computer graphics processing pipeline. In this thesis we propose novel approaches to including semantic knowledge into reconstruction processes using template based shape processing. We formulate shape reconstruction as a deformable template fitting process, where we try to fit a given template model to the sampled data. This approach allows us to present novel solutions to several fundamental problems in the area of shape reconstruction. We address static problems like constrained texture mapping and semantically meaningful hole-filling in surface reconstruction from 3D scans, temporal problems such as mesh based performance capture, and finally dynamic problems like the estimation of physically based material parameters of animated templates.Analoge Signale müssen digitalisiert werden um sie auf modernen Computern speichern und verarbeiten zu können. Für viele Signale, wie zum Beispiel Bilder oder Tondaten, existieren heutzutage effektive und effiziente Digitalisierungstechniken. Aus den so gewonnenen Daten können die ursprünglichen Signale hinreichend akkurat wiederhergestellt werden. Im Gegensatz dazu stellt das präzise und effiziente Digitalisieren und Rekonstruieren von 3D- oder gar 4D-Geometrie immer noch eine Herausforderung dar. So führen Verdeckungen und Fehler während der Digitalisierung zu Löchern und verrauschten Meßdaten. Die Erforschung von akkuraten Rekonstruktionsmethoden für diese groben digitalen Daten ist daher ein entscheidender Schritt in der Entwicklung moderner Verarbeitungsmethoden in der Computergrafik. In dieser Dissertation wird veranschaulicht, wie deformierbare geometrische Modelle als Vorlage genutzt werden können, um semantische Informationen in die robuste Rekonstruktion von 3D- und 4D Geometrie einfließen zu lassen. Dadurch wird es möglich, neue Lösungsansätze für mehrere grundlegenden Probleme der Computergrafik zu entwickeln. So können mit dieser Technik Löcher in digitalisierten 3D Modellen semantisch sinnvoll aufgefüllt, oder detailgetreue virtuelle Kopien von Darstellern und ihrer dynamischen Kleidung zu erzeugt werden

Prediction of 3D Body Parts from Face Shape and Anthropometric Measurements

While 3D body models have been vastly studied in the last decade, acquiring accurate models from the sparse information about the subject and few computational resources is still a main open challenge. In this paper, we propose a methodology for finding the most relevant anthropometric measurements and facial shape features for the prediction of the shape of an arbitrary segmented body part. For the evaluation, we selected 12 features that are easy to obtain or measure including age, gender, weight and height; and augmented them with shape parameters extracted from 3D facial scans. For each subset of features, with and without facial parameters, we predicted the shape of 5 segmented body parts using linear and non-linear regression models. The results show that the modeling approach is effective and giving sub cm reconstruction accuracy. Moreover, adding face shape features always significantly improves the prediction