A Survey on Thinking in Visual Generation: Reasoning, Planning, and Cognition

The landscape of visual generation has dramatically evolved, moving beyond mere pixel manipulation towards synthesizing content endowed with human-like intelligence. This survey delves into the burgeoning field of "thinking" in visual generation, where models are increasingly equipped with reasoning, planning, and cognitive capabilities. We argue that for visual generative models to produce high-quality, semantically meaningful, and controllable outputs, they must transcend purely data-driven synthesis and incorporate higher-level cognitive processes. This paper systematically reviews the state-of-the-art, categorizing advancements into distinct sections focusing on reasoning mechanisms that infer semantic relationships, planning approaches that decompose complex generation into multi-step processes, and broader cognitive perspectives encompassing intent understanding, adaptive learning, and creative aspects. We explore how structured knowledge representations, iterative refinement strategies, and neuro-symbolic integrations are enabling models to generate visuals that reflect logical coherence and contextual appropriateness. Furthermore, we investigate the emergence of metacognitive abilities, self-cognition, and human-centric design thinking within generative AI. Finally, we meticulously discuss current challenges, including issues related to scalability, robustness, evaluation of cognitive capabilities, and the critical ethical considerations pertinent to advanced visual AI systems. We conclude by outlining promising future research directions, such as advanced neuro-symbolic integration, multimodal cognitive generation, and embodied intelligence, hoping to inspire further research at the intersection of generative AI and cognitive science.