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Social media platforms have led to online consumption communities, or fandoms, that involve complex networks of ancillary creators and consumers focused on some core product or intellectual property. For example, video game communities include networks of players and content creators centered around a specific video game. These networks are complex in that video game publishers often sponsor creators, but creators and publishers may have divergent incentives. Specifically, creators can potentially benefit from content that builds their own following at the expense of the core game. Our research investigates the relationship between consuming live-streamed content and engagement with a specific video game. We examine the causal effect of viewing live-streamed content on subsequent gameplay for a specific game, using an unexpected service interruption of the livestreaming platform and time zone differences among users. We find live-streamed content significantly increases gameplay as a 10% increase in live-streamed viewing minutes results in a 3.08% increase in gameplay minutes. We also explore how this effect varies by user loyalty to different types of streamer channels (firm-owned, mega, and micro). The positive effects of live-streamed content are greatest for micro-streamers and smallest for mega-streamers. These findings are salient for firms allocating sponsorship resources., Comment: 51 pages, 9 Tables, 6 Figures

Creating 3D head avatars is a significant yet challenging task for many applicated scenarios. Previous studies have set out to learn 3D human head generative models using massive 2D image data. Although these models are highly generalizable for human appearance, their result models are not 360$^\circ$-renderable, and the predicted 3D geometry is unreliable. Therefore, such results cannot be used in VR, game modeling, and other scenarios that require 360$^\circ$-renderable 3D head models. An intuitive idea is that 3D head models with limited amount but high 3D accuracy are more reliable training data for a high-quality 3D generative model. In this vein, we delve into how to learn a native generative model for 360$^\circ$ full head from a limited 3D head dataset. Specifically, three major problems are studied: 1) how to effectively utilize various representations for generating the 360$^\circ$-renderable human head; 2) how to disentangle the appearance, shape, and motion of human faces to generate a 3D head model that can be edited by appearance and driven by motion; 3) and how to extend the generalization capability of the generative model to support downstream tasks. Comprehensive experiments are conducted to verify the effectiveness of the proposed model. We hope the proposed models and artist-designed dataset can inspire future research on learning native generative 3D head models from limited 3D datasets.

Creating a 360{\deg} parametric model of a human head is a very challenging task. While recent advancements have demonstrated the efficacy of leveraging synthetic data for building such parametric head models, their performance remains inadequate in crucial areas such as expression-driven animation, hairstyle editing, and text-based modifications. In this paper, we build a dataset of artist-designed high-fidelity human heads and propose to create a novel parametric 360{\deg} renderable parametric head model from it. Our scheme decouples the facial motion/shape and facial appearance, which are represented by a classic parametric 3D mesh model and an attached neural texture, respectively. We further propose a training method for decompositing hairstyle and facial appearance, allowing free-swapping of the hairstyle. A novel inversion fitting method is presented based on single image input with high generalization and fidelity. To the best of our knowledge, our model is the first parametric 3D full-head that achieves 360{\deg} free-view synthesis, image-based fitting, appearance editing, and animation within a single model. Experiments show that facial motions and appearances are well disentangled in the parametric space, leading to SOTA performance in rendering and animating quality. The code and SynHead100 dataset are released at https://nju-3dv.github.io/projects/Head360., Comment: ECCV 2024

Recent geospatial machine learning studies have shown that the results of model evaluation via cross-validation (CV) are strongly affected by the dissimilarity between the sample data and the prediction locations. In this paper, we propose a method to quantify such a dissimilarity in the interval 0 to 100% and from the perspective of the data feature space. The proposed method is based on adversarial validation, which is an approach that can check whether sample data and prediction locations can be separated with a binary classifier. The proposed method is called dissimilarity quantification by adversarial validation (DAV). To study the effectiveness and general?ity of DAV, we tested it on a series of experiments based on both synthetic and real datasets and with gradually increasing dissimilarities. Results show that DAV effectively quantified dissimilarity across the entire range of values. Next to this, we studied how dissimilarity affects CV methods' evaluations by comparing the results of random CV method (RDM-CV) and of two geospatial CV methods, namely, block and spatial+ CV (BLK-CV and SP-CV). Our results showed the evaluations follow similar patterns in all datasets and predictions: when dissimilarity is low (usually lower than 30%), RDM-CV provides the most accurate evaluation results. As dissimilarity increases, geospatial CV methods, especially SP-CV, become more and more accurate and even outperform RDM-CV. When dissimilarity is high (>=90%), no CV method provides accurate evaluations. These results show the importance of considering feature space dissimilarity when working with geospatial machine learning predictions and can help researchers and practitioners to select more suitable CV methods for evaluating their predictions., Comment: 19 pages, 11 figures

``Exact'' laws for evaluating cascade rates, tracing back to the Kolmogorov ``4/5'' law, have been extended to many systems of interest including magnetohydrodynamics (MHD), and compressible flows of the magnetofluid and ordinary fluid types. It is understood that implementations may be limited by the quantity of available data and by the lack of turbulence symmetry. Assessment of the accuracy and feasibility of such ``third-order'' (or Yaglom) relations is most effectively accomplished by examining the von Karman-Howarth equation in increment form, a framework from which the third-order laws are derived as asymptotic approximations. Using this approach, we examine the context of third-order laws for incompressible MHD in some detail. The simplest versions rely on the assumption of isotropy and the presence of a well-defined inertial range, while related procedures generalize the same idea to arbitrary rotational symmetries. Conditions for obtaining correct and accurate values of the dissipation rate from these laws based on several sampling and fitting strategies are investigated using results from simulations. The questions we address are of particular relevance to sampling of solar wind turbulence by one or more spacecraft., Comment: 19 pages, 12 figures