Core Viewpoint - The article discusses the advancements in computer vision (CV) and image generation capabilities brought by the new GPT-4o model, highlighting its potential to disrupt existing tools and methodologies in the field [1][2]. Group 1: Technological Advancements - GPT-4o introduces native multimodal image generation, expanding the functionalities of AI tools beyond traditional applications [2][12]. - The image generation process in GPT-4o is based on a self-regressive model, differing from the diffusion model used in DALL·E, which allows for better adherence to instructions and enhanced image editing capabilities [15][19]. - Observations suggest that the image generation may involve a multi-scale self-regressive combination, where a rough image is generated first, followed by detail filling while the rough shape evolves [17][19]. Group 2: Industry Impact - The advancements in GPT-4o's capabilities have raised concerns among designers and computer vision researchers, indicating a significant shift in the competitive landscape of AI tools [6][10]. - OpenAI's approach of scaling foundational models to achieve these capabilities has surprised many in the industry, suggesting a new trend in AI development [12][19]. - The potential for GPT-4o to enhance applications in autonomous driving has been noted, with implications for future developments in this sector [10]. Group 3: Community Engagement - The article encourages community members to share their experiences and innovative uses of GPT-4o, fostering a collaborative environment for exploring AI applications [26].

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