RESUMEN
The current study examined the relationship between mothers' selfie-related behaviors and adolescents' cosmetic surgery consideration, as well as the mediating effects of the adolescents' selfie-related behaviors, body surveillance, and facial dissatisfaction. A total of 541 mother-child dyads with adolescents averaging 16.55 years old, was recruited. The path analysis revealed that mothers' selfie-related behaviors were not directly related to adolescents' consideration of cosmetic surgery, but the link was mediated by the adolescents' selfie-related behaviors, body surveillance, and facial dissatisfaction. Specifically, there was a mediating effect of adolescents' facial dissatisfaction, as well as serial mediating effects of adolescents' selfie-related behaviors and facial dissatisfaction, of adolescents' selfie-related behaviors and body surveillance, and of adolescents' selfie-related behaviors, body surveillance and facial dissatisfaction. Additionally, we did not find a significant gender difference in the model. These findings provide further insights into the association between a mother's selfie activities and adolescent children's cosmetic surgery consideration.
RESUMEN
Pose-guided person image generation and animation aim to transform a source person image to target poses. These tasks require spatial manipulation of source data. However, Convolutional Neural Networks are limited by the lack of ability to spatially transform the inputs. In this paper, we propose a differentiable global-flow local-attention framework to reassemble the inputs at the feature level. This framework first estimates global flow fields between sources and targets. Then, corresponding local source feature patches are sampled with content-aware local attention coefficients. We show that our framework can spatially transform the inputs in an efficient manner. Meanwhile, we further model the temporal consistency for the person image animation task to generate coherent videos. The experiment results of both image generation and animation tasks demonstrate the superiority of our model. Besides, additional results of novel view synthesis and face image animation show that our model is applicable to other tasks requiring spatial transformation. The source code of our project is available at https://github.com/RenYurui/Global-Flow-Local-Attention.