Research on Collaborative Creation of Humanoid Robot Visual Design Based on Generative Artificial Intelligence

There are industry pain points in the visual design of humanoid robots, such as creative reproduction deviation, distorted appearance, and incompatibility between aesthetic design and engineering structure. This article proposes a visual collaborative creation method for CAM DiT humanoid robots based on generative artificial intelligence, constructing a dual driven overall architecture of visual creativity generation and engineering constraint control. The method relies on a multimodal visual information encoding alignment mechanism, integrates text style semantics and sketch geometric features, and accurately restores design creativity. By embedding 12 core engineering constraints into the DiT generation model, the integration of appearance aesthetics and motion feasibility can be achieved. At the same time, establish a feasibility verification feedback mechanism and a human-machine collaborative iterative strategy for preference scoring and local mask correction, to achieve precise optimization of visual design. This article constructs an exclusive dataset for robot vision design, adding dual evaluation indicators of visual aesthetics and engineering constraints. The effectiveness of the method is verified through comparative experiments, module ablation experiments, sensitivity analysis, and user experience testing. The results indicate that this method can effectively improve the quality of robot vision generation and collaborative creation efficiency, providing new technical support for the intelligent and engineering implementation of humanoid robot vision design.