AffordPose: A Large-scale Dataset of Hand-Object Interactions with Affordance-driven Hand Pose

How human interact with objects depends on the functional roles of the target objects, which introduces the problem of affordance-aware hand-object interaction. It requires a large number of human demonstrations for the learning and understanding of plausible and appropriate hand-object interactions. In this work, we present AffordPose, a large-scale dataset of hand-object interactions with affordance-driven hand pose. We first annotate the specific part-level affordance labels for each object, e.g. twist, pull, handle-grasp, etc, instead of the general intents such as use or handover, to indicate the purpose and guide the localization of the hand-object interactions. The fine-grained hand-object interactions reveal the influence of hand-centered affordances on the detailed arrangement of the hand poses, yet also exhibit a certain degree of diversity. We collect a total of 26.7K hand-object interactions, each including the 3D object shape, the part-level affordance label, and the manually adjusted hand poses. The comprehensive data analysis shows the common characteristics and diversity of hand-object interactions per affordance via the parameter statistics and contacting computation. We also conduct experiments on the tasks of hand-object affordance understanding and affordance-oriented hand-object interaction generation, to validate the effectiveness of our dataset in learning the fine-grained hand-object interactions. Project page: https://github.com/GentlesJan/AffordPose.Comment: Accepted by ICCV 202

Advances in Data-Driven Analysis and Synthesis of 3D Indoor Scenes

This report surveys advances in deep learning-based modeling techniques that address four different 3D indoor scene analysis tasks, as well as synthesis of 3D indoor scenes. We describe different kinds of representations for indoor scenes, various indoor scene datasets available for research in the aforementioned areas, and discuss notable works employing machine learning models for such scene modeling tasks based on these representations. Specifically, we focus on the analysis and synthesis of 3D indoor scenes. With respect to analysis, we focus on four basic scene understanding tasks -- 3D object detection, 3D scene segmentation, 3D scene reconstruction and 3D scene similarity. And for synthesis, we mainly discuss neural scene synthesis works, though also highlighting model-driven methods that allow for human-centric, progressive scene synthesis. We identify the challenges involved in modeling scenes for these tasks and the kind of machinery that needs to be developed to adapt to the data representation, and the task setting in general. For each of these tasks, we provide a comprehensive summary of the state-of-the-art works across different axes such as the choice of data representation, backbone, evaluation metric, input, output, etc., providing an organized review of the literature. Towards the end, we discuss some interesting research directions that have the potential to make a direct impact on the way users interact and engage with these virtual scene models, making them an integral part of the metaverse.Comment: Published in Computer Graphics Forum, Aug 202

Two-Dimensional Far Field Source Locating Method with Nonprior Velocity

Relative position of seismic source and sensors has great influence on locating accuracy, particularly in far field conditions, and the accuracy will decrease seriously due to limited calculation precision and prior velocity error. In order to improve the locating accuracy of far field sources by isometric placed sensors in a straight line, a new locating method with nonprior velocity is proposed. After exhaustive research, this paper states that the hyperbola which is used for locating will be very close to its asymptote when seismic source locates in far field of sensors; therefore, the locating problem with prior velocity is equivalent to solving linear equations and the problem with nonprior velocity is equivalent to a nonlinear optimization problem with respect to the unknown velocity. And then, this paper proposed a new locating method based on a one-variable objective function with respect to the unknown velocity. Numerical experiments show that the proposed method has faster convergence speed, higher accuracy, and better stability

Alterations in the gut microbiota and serum metabolomics of spontaneous cholestasis caused by loss of FXR signal in mice

Background: Farnesoid X receptor (FXR) is a key metabolic target of bile acids (BAs) and is also a target for drugs against several liver diseases. However, the contribution of FXR in the pathogenesis of cholestasis is still not fully understood. The purpose of this study is to provide a comprehensive insight into the metabolic properties of FXR-involved cholestasis in mice.Materials and methods: In this study, an alpha-naphthylisothiocyanate (ANIT)-induced cholestasis mouse model and FXR−/− mice were established to investigate the effect of FXR on cholestasis. The effect of FXR on liver and ileal pathology was evaluated. Simultaneously, Untargeted metabolomics combined with 16s rRNA gene sequencing analysis was applied to reveal the involvement of FXR in the pathogenesis of cholestasis.Results: The results showed that ANIT (75 mg/kg) induced marked cholestasis in WT and FXR −/− mice. It is noteworthy that FXR−/− mice developed spontaneous cholestasis. Compared with WT mice, significant liver and ileal tissue damage were found. In addition, 16s rRNA gene sequencing analysis revealed gut microbiota dysbiosis in FXR−/− mice and ANIT-induced cholestasis mice. Differential biomarkers associated with the pathogenesis of cholestasis caused by FXR knockout were screened using untargeted metabolomics. Notably, Lactobacillus_ johnsonii_FI9785 has a high correlation with the differential biomarkers associated with the pathogenesis and progression of cholestasis caused by FXR knockout.Conclusion: Our results implied that the disorder of the intestinal flora caused by FXR knockout can also interfere with the metabolism. This study provides novel insights into the FXR-related mechanisms of cholestasis