Exhibiting Class: Art Exhibition and the New Chinese Middle Class

Kanzhan, translated at “going to exhibitions,” has emerged as one of the most popular leisure activities in urban China. Contemporary art exhibitions cover a wide range of subjects, including world-renown artists, jewelry and fashion brands, and pop-up museums. More and more visitors are taking art exhibitions experience as a way to exhibit their personal taste, which reflect the rise of middle-class values such as individuality and self-development in China. This paper is an anthropological exploration of the relationship between visitors and art exhibits and what those art exhibitions tell about the new middle class in China. My research is based on original field research in the summer of 2018 and winter of 2019. I conducted participant observation and semi-structured interviews in art exhibitions in the city of Beijing and Shanghai. Drawing on anthropological theories of cosmopolitanism, body and emotion, and photography and self-presentation, I argue that going to art exhibitions is a critical means of performing and reinforcing one’s middle-class identity and aspirations in contemporary China. As such, the thesis contributes to the anthropological understanding of the role of aesthetics and taste in the production of class

Relighting4D: Neural Relightable Human from Videos

Human relighting is a highly desirable yet challenging task. Existing works either require expensive one-light-at-a-time (OLAT) captured data using light stage or cannot freely change the viewpoints of the rendered body. In this work, we propose a principled framework, Relighting4D, that enables free-viewpoints relighting from only human videos under unknown illuminations. Our key insight is that the space-time varying geometry and reflectance of the human body can be decomposed as a set of neural fields of normal, occlusion, diffuse, and specular maps. These neural fields are further integrated into reflectance-aware physically based rendering, where each vertex in the neural field absorbs and reflects the light from the environment. The whole framework can be learned from videos in a self-supervised manner, with physically informed priors designed for regularization. Extensive experiments on both real and synthetic datasets demonstrate that our framework is capable of relighting dynamic human actors with free-viewpoints.Comment: ECCV 2022; Project Page https://frozenburning.github.io/projects/relighting4d Codes are available at https://github.com/FrozenBurning/Relighting4

Semantic Image Segmentation via Deep Parsing Network

This paper addresses semantic image segmentation by incorporating rich information into Markov Random Field (MRF), including high-order relations and mixture of label contexts. Unlike previous works that optimized MRFs using iterative algorithm, we solve MRF by proposing a Convolutional Neural Network (CNN), namely Deep Parsing Network (DPN), which enables deterministic end-to-end computation in a single forward pass. Specifically, DPN extends a contemporary CNN architecture to model unary terms and additional layers are carefully devised to approximate the mean field algorithm (MF) for pairwise terms. It has several appealing properties. First, different from the recent works that combined CNN and MRF, where many iterations of MF were required for each training image during back-propagation, DPN is able to achieve high performance by approximating one iteration of MF. Second, DPN represents various types of pairwise terms, making many existing works as its special cases. Third, DPN makes MF easier to be parallelized and speeded up in Graphical Processing Unit (GPU). DPN is thoroughly evaluated on the PASCAL VOC 2012 dataset, where a single DPN model yields a new state-of-the-art segmentation accuracy.Comment: To appear in International Conference on Computer Vision (ICCV) 201