790 research outputs found

    Reconstruction and Synthesis of Human-Scene Interaction

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    In this thesis, we argue that the 3D scene is vital for understanding, reconstructing, and synthesizing human motion. We present several approaches which take the scene into consideration in reconstructing and synthesizing Human-Scene Interaction (HSI). We first observe that state-of-the-art pose estimation methods ignore the 3D scene and hence reconstruct poses that are inconsistent with the scene. We address this by proposing a pose estimation method that takes the 3D scene explicitly into account. We call our method PROX for Proximal Relationships with Object eXclusion. We leverage the data generated using PROX and build a method to automatically place 3D scans of people with clothing in scenes. The core novelty of our method is encoding the proximal relationships between the human and the scene in a novel HSI model, called POSA for Pose with prOximitieS and contActs. POSA is limited to static HSI, however. We propose a real-time method for synthesizing dynamic HSI, which we call SAMP for Scene-Aware Motion Prediction. SAMP enables virtual humans to navigate cluttered indoor scenes and naturally interact with objects. Data-driven kinematic models, like SAMP, can produce high-quality motion when applied in environments similar to those shown in the dataset. However, when applied to new scenarios, kinematic models can struggle to generate realistic behaviors that respect scene constraints. In contrast, we present InterPhys which uses adversarial imitation learning and reinforcement learning to train physically-simulated characters that perform scene interaction tasks in a physical and life-like manner

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well

    FLARE: Fast Learning of Animatable and Relightable Mesh Avatars

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    Our goal is to efficiently learn personalized animatable 3D head avatars from videos that are geometrically accurate, realistic, relightable, and compatible with current rendering systems. While 3D meshes enable efficient processing and are highly portable, they lack realism in terms of shape and appearance. Neural representations, on the other hand, are realistic but lack compatibility and are slow to train and render. Our key insight is that it is possible to efficiently learn high-fidelity 3D mesh representations via differentiable rendering by exploiting highly-optimized methods from traditional computer graphics and approximating some of the components with neural networks. To that end, we introduce FLARE, a technique that enables the creation of animatable and relightable mesh avatars from a single monocular video. First, we learn a canonical geometry using a mesh representation, enabling efficient differentiable rasterization and straightforward animation via learned blendshapes and linear blend skinning weights. Second, we follow physically-based rendering and factor observed colors into intrinsic albedo, roughness, and a neural representation of the illumination, allowing the learned avatars to be relit in novel scenes. Since our input videos are captured on a single device with a narrow field of view, modeling the surrounding environment light is non-trivial. Based on the split-sum approximation for modeling specular reflections, we address this by approximating the pre-filtered environment map with a multi-layer perceptron (MLP) modulated by the surface roughness, eliminating the need to explicitly model the light. We demonstrate that our mesh-based avatar formulation, combined with learned deformation, material, and lighting MLPs, produces avatars with high-quality geometry and appearance, while also being efficient to train and render compared to existing approaches.Comment: 15 pages, Accepted: ACM Transactions on Graphics (Proceedings of SIGGRAPH Asia), 202

    RenderMe-360: A Large Digital Asset Library and Benchmarks Towards High-fidelity Head Avatars

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    Synthesizing high-fidelity head avatars is a central problem for computer vision and graphics. While head avatar synthesis algorithms have advanced rapidly, the best ones still face great obstacles in real-world scenarios. One of the vital causes is inadequate datasets -- 1) current public datasets can only support researchers to explore high-fidelity head avatars in one or two task directions; 2) these datasets usually contain digital head assets with limited data volume, and narrow distribution over different attributes. In this paper, we present RenderMe-360, a comprehensive 4D human head dataset to drive advance in head avatar research. It contains massive data assets, with 243+ million complete head frames, and over 800k video sequences from 500 different identities captured by synchronized multi-view cameras at 30 FPS. It is a large-scale digital library for head avatars with three key attributes: 1) High Fidelity: all subjects are captured by 60 synchronized, high-resolution 2K cameras in 360 degrees. 2) High Diversity: The collected subjects vary from different ages, eras, ethnicities, and cultures, providing abundant materials with distinctive styles in appearance and geometry. Moreover, each subject is asked to perform various motions, such as expressions and head rotations, which further extend the richness of assets. 3) Rich Annotations: we provide annotations with different granularities: cameras' parameters, matting, scan, 2D/3D facial landmarks, FLAME fitting, and text description. Based on the dataset, we build a comprehensive benchmark for head avatar research, with 16 state-of-the-art methods performed on five main tasks: novel view synthesis, novel expression synthesis, hair rendering, hair editing, and talking head generation. Our experiments uncover the strengths and weaknesses of current methods. RenderMe-360 opens the door for future exploration in head avatars.Comment: Technical Report; Project Page: 36; Github Link: https://github.com/RenderMe-360/RenderMe-36

    Geometric Learning on Graph Structured Data

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    Graphs provide a ubiquitous and universal data structure that can be applied in many domains such as social networks, biology, chemistry, physics, and computer science. In this thesis we focus on two fundamental paradigms in graph learning: representation learning and similarity learning over graph-structured data. Graph representation learning aims to learn embeddings for nodes by integrating topological and feature information of a graph. Graph similarity learning brings into play with similarity functions that allow to compute similarity between pairs of graphs in a vector space. We address several challenging issues in these two paradigms, designing powerful, yet efficient and theoretical guaranteed machine learning models that can leverage rich topological structural properties of real-world graphs. This thesis is structured into two parts. In the first part of the thesis, we will present how to develop powerful Graph Neural Networks (GNNs) for graph representation learning from three different perspectives: (1) spatial GNNs, (2) spectral GNNs, and (3) diffusion GNNs. We will discuss the model architecture, representational power, and convergence properties of these GNN models. Specifically, we first study how to develop expressive, yet efficient and simple message-passing aggregation schemes that can go beyond the Weisfeiler-Leman test (1-WL). We propose a generalized message-passing framework by incorporating graph structural properties into an aggregation scheme. Then, we introduce a new local isomorphism hierarchy on neighborhood subgraphs. We further develop a novel neural model, namely GraphSNN, and theoretically prove that this model is more expressive than the 1-WL test. After that, we study how to build an effective and efficient graph convolution model with spectral graph filters. In this study, we propose a spectral GNN model, called DFNets, which incorporates a novel spectral graph filter, namely feedback-looped filters. As a result, this model can provide better localization on neighborhood while achieving fast convergence and linear memory requirements. Finally, we study how to capture the rich topological information of a graph using graph diffusion. We propose a novel GNN architecture with dynamic PageRank, based on a learnable transition matrix. We explore two variants of this GNN architecture: forward-euler solution and invariable feature solution, and theoretically prove that our forward-euler GNN architecture is guaranteed with the convergence to a stationary distribution. In the second part of this thesis, we will introduce a new optimal transport distance metric on graphs in a regularized learning framework for graph kernels. This optimal transport distance metric can preserve both local and global structures between graphs during the transport, in addition to preserving features and their local variations. Furthermore, we propose two strongly convex regularization terms to theoretically guarantee the convergence and numerical stability in finding an optimal assignment between graphs. One regularization term is used to regularize a Wasserstein distance between graphs in the same ground space. This helps to preserve the local clustering structure on graphs by relaxing the optimal transport problem to be a cluster-to-cluster assignment between locally connected vertices. The other regularization term is used to regularize a Gromov-Wasserstein distance between graphs across different ground spaces based on degree-entropy KL divergence. This helps to improve the matching robustness of an optimal alignment to preserve the global connectivity structure of graphs. We have evaluated our optimal transport-based graph kernel using different benchmark tasks. The experimental results show that our models considerably outperform all the state-of-the-art methods in all benchmark tasks

    LiveHand: Real-time and Photorealistic Neural Hand Rendering

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    The human hand is the main medium through which we interact with our surroundings. Hence, its digitization is of uttermost importance, with direct applications in VR/AR, gaming, and media production amongst other areas. While there are several works for modeling the geometry and articulations of hands, little attention has been dedicated to capturing photo-realistic appearance. In addition, for applications in extended reality and gaming, real-time rendering is critical. In this work, we present the first neural-implicit approach to photo-realistically render hands in real-time. This is a challenging problem as hands are textured and undergo strong articulations with various pose-dependent effects. However, we show that this can be achieved through our carefully designed method. This includes training on a low-resolution rendering of a neural radiance field, together with a 3D-consistent super-resolution module and mesh-guided space canonicalization and sampling. In addition, we show the novel application of a perceptual loss on the image space is critical for achieving photorealism. We show rendering results for several identities, and demonstrate that our method captures pose- and view-dependent appearance effects. We also show a live demo of our method where we photo-realistically render the human hand in real-time for the first time in literature. We ablate all our design choices and show that our design optimizes for both photorealism and rendering speed. Our code will be released to encourage further research in this area.Comment: 11 pages, 8 figure

    Pathway to Future Symbiotic Creativity

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    This report presents a comprehensive view of our vision on the development path of the human-machine symbiotic art creation. We propose a classification of the creative system with a hierarchy of 5 classes, showing the pathway of creativity evolving from a mimic-human artist (Turing Artists) to a Machine artist in its own right. We begin with an overview of the limitations of the Turing Artists then focus on the top two-level systems, Machine Artists, emphasizing machine-human communication in art creation. In art creation, it is necessary for machines to understand humans' mental states, including desires, appreciation, and emotions, humans also need to understand machines' creative capabilities and limitations. The rapid development of immersive environment and further evolution into the new concept of metaverse enable symbiotic art creation through unprecedented flexibility of bi-directional communication between artists and art manifestation environments. By examining the latest sensor and XR technologies, we illustrate the novel way for art data collection to constitute the base of a new form of human-machine bidirectional communication and understanding in art creation. Based on such communication and understanding mechanisms, we propose a novel framework for building future Machine artists, which comes with the philosophy that a human-compatible AI system should be based on the "human-in-the-loop" principle rather than the traditional "end-to-end" dogma. By proposing a new form of inverse reinforcement learning model, we outline the platform design of machine artists, demonstrate its functions and showcase some examples of technologies we have developed. We also provide a systematic exposition of the ecosystem for AI-based symbiotic art form and community with an economic model built on NFT technology. Ethical issues for the development of machine artists are also discussed

    Engineering for a changing world: 60th Ilmenau Scientific Colloquium, Technische Universität Ilmenau, September 04-08, 2023 : programme

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    In 2023, the Ilmenau Scientific Colloquium is once more organised by the Department of Mechanical Engineering. The title of this year’s conference “Engineering for a Changing World” refers to limited natural resources of our planet, to massive changes in cooperation between continents, countries, institutions and people – enabled by the increased implementation of information technology as the probably most dominant driver in many fields. The Colloquium, supplemented by workshops, is characterised but not limited to the following topics: – Precision engineering and measurement technology Nanofabrication – Industry 4.0 and digitalisation in mechanical engineering – Mechatronics, biomechatronics and mechanism technology – Systems engineering – Productive teaming - Human-machine collaboration in the production environment The topics are oriented on key strategic aspects of research and teaching in Mechanical Engineering at our university

    Aggressive saliency-aware point cloud compression

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    The increasing demand for accurate representations of 3D scenes, combined with immersive technologies has led point clouds to extensive popularity. However, quality point clouds require a large amount of data and therefore the need for compression methods is imperative. In this paper, we present a novel, geometry-based, end-to-end compression scheme, that combines information on the geometrical features of the point cloud and the user's position, achieving remarkable results for aggressive compression schemes demanding very small bit rates. After separating visible and non-visible points, four saliency maps are calculated, utilizing the point cloud's geometry and distance from the user, the visibility information, and the user's focus point. A combination of these maps results in a final saliency map, indicating the overall significance of each point and therefore quantizing different regions with a different number of bits during the encoding process. The decoder reconstructs the point cloud making use of delta coordinates and solving a sparse linear system. Evaluation studies and comparisons with the geometry-based point cloud compression (G-PCC) algorithm by the Moving Picture Experts Group (MPEG), carried out for a variety of point clouds, demonstrate that the proposed method achieves significantly better results for small bit rates
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