Core Viewpoint - EmoStyle aims to simplify the process of emotional image stylization by allowing users to express their desired emotions, which the system then translates into artistic images without requiring artistic skills [4][8]. Group 1: EmoStyle Overview - EmoStyle is developed by the Visual Computing Research Center at Shenzhen University, led by Professor Huang Hui, focusing on interdisciplinary innovation in computer graphics and visual analysis [2]. - The project introduces Affective Image Stylization (AIS), which seeks to evoke specific emotions while maintaining semantic consistency with the original image [5][8]. Group 2: Challenges and Contributions - The main challenges addressed include the lack of training data for "content-emotion-stylization" image triplets and establishing a mapping between emotion and style [5][8]. - EmoStyleSet is constructed as the first AIS dataset, containing 10,041 high-quality triplets to advance visual emotion research [8]. Group 3: Methodology - EmoStyle incorporates two key modules: the Emotion-Content Reasoner, which determines the most suitable style based on the content image and target emotion, and the Style Quantizer, which discretizes style features for better interpretability [14][16]. - The training process involves optimizing the network through style loss, flow matching loss, and alignment loss to balance style similarity, pixel similarity, and emotional correctness [18][19]. Group 4: Experimental Results - EmoStyle demonstrates superior performance in emotional expression and content retention compared to other methods, achieving a balance that results in aesthetically pleasing and emotionally impactful stylized images [22][25]. - Quantitative evaluations show EmoStyle surpassing other methods in semantic, style, and emotional metrics, indicating its effectiveness in the AIS task [26]. Group 5: Future Directions - EmoStyle has potential applications beyond image stylization, including text-to-image generation, allowing for the creation of emotionally expressive images based on textual descriptions [31]. - The research group plans to continue exploring the intersection of affective computing and generative AI, contributing new ideas and methods to the field [34].