Intelligent bots are evolving with the development of artificial intelligence, especially the deep learning method. Many skills like semantic judgment, speech recognition, and text generation have been added, making bots more like real persons. The latest ones, such as Microsoft XiaoIce, Amazon Alexa, and Apple Siri, focus on enhancing general functionalities but still overlook the personality of the bot itself nevertheless, e.g., unchanging name and its virtual appearance. To further personalize the user experience, we desire to make the appearance of intelligent bots more diverse, i.e., appearing capable of autonomously changing its characteristic appearance according to users’ contexts like the changing geolocation. In this paper, we designe a personalized appearance transformation framework for the next generation intelligent bots. Specifically, Multi-modal crowd-intelligence technology is used for differential analysis of various regions, and generative adversarial network (GAN) is customized to render the bot appearance target domain. We also collecte new region-specific data sets from social media platforms, implement a fully-fledged prototype, and demonstratedthe effectiveness of our proposed framework.