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We present an interactive visual framework named InternGPT, or iGPT forshort. The framework integrates chatbots that have planning and reasoningcapabilities, such as ChatGPT, with non-verbal instructions like pointingmovements that enable users to directly manipulate images or videos on thescreen. Pointing (including gestures, cursors, etc.) movements can provide moreflexibility and precision in performing vision-centric tasks that requirefine-grained control, editing, and generation of visual content. The nameInternGPT stands for \textbf{inter}action, \textbf{n}onverbal, and\textbf{chat}bots. Different from existing interactive systems that rely onpure language, by incorporating pointing instructions, the proposed iGPTsignificantly improves the efficiency of communication between users andchatbots, as well as the accuracy of chatbots in vision-centric tasks,especially in complicated visual scenarios where the number of objects isgreater than 2. Additionally, in iGPT, an auxiliary control mechanism is usedto improve the control capability of LLM, and a large vision-language modeltermed Husky is fine-tuned for high-quality multi-modal dialogue (impressingChatGPT-3.5-turbo with 93.89\% GPT-4 Quality). We hope this work can spark newideas and directions for future interactive visual systems. Welcome to watchthe code at https://github.com/OpenGVLab/InternGPT.

InternGPT: Solving Vision-Centric Tasks by Interacting with ChatGPT Beyond Language