Yingqing Xu

Chenyu Yang, Wanrong He, Yingqing Xu et al.

Most existing methods view makeup transfer as transferring color distributions of different facial regions and ignore details such as eye shadows and blushes. Besides, they only achieve controllable transfer within predefined fixed regions. This paper emphasizes the transfer of makeup details and steps towards more flexible controls. To this end, we propose Exquisite and locally editable GAN for makeup transfer (EleGANt). It encodes facial attributes into pyramidal feature maps to preserves high-frequency information. It uses attention to extract makeup features from the reference and adapt them to the source face, and we introduce a novel Sow-Attention Module that applies attention within shifted overlapped windows to reduce the computational cost. Moreover, EleGANt is the first to achieve customized local editing within arbitrary areas by corresponding editing on the feature maps. Extensive experiments demonstrate that EleGANt generates realistic makeup faces with exquisite details and achieves state-of-the-art performance. The code is available at https://github.com/Chenyu-Yang-2000/EleGANt.

Siyu Zha, Weijing Liu, Fei Qin et al.

Generative artificial intelligence (GenAI) is increasingly embedded in computer-supported collaborative learning (CSCL), yet little empirical research has unpacked how different configurations of AI participation reshape collaborative processes. This study investigates how GenAI configuration shapes collaborative regulation in authentic classroom settings. Two eighth-grade classes engaged in small-group creative problem-solving under two conditions: a shared-AI configuration, in which each group interacted with a single AI mentor, and an individual-AI configuration, in which each student accessed a personal AI instance. Using multi-layer discourse coding combined with lag sequential analysis (LSA) and ordered network analysis (ONA), we examined interaction distribution, AI-student coupling, shared regulation processes, and teacher orchestration. Results reveal distinct regulatory dynamics across configurations. Shared AI access promoted convergence-oriented collaboration, with stronger alignment of shared regulatory states and more coordinated group-level reasoning. In contrast, individual AI access distributed support across learners, producing more exploratory and evaluative cycles but also more fragmented interaction patterns, accompanied by increased teacher intervention to manage divergence. These findings suggest that AI configuration functions as a structural design variable that reorganizes the regulatory ecology of classroom collaboration.