Comparative analysis of GAN and diffusion models for computer graphics object generation

This study compares Generative Adversarial Networks (GANs) and diffusion models for computer graphics object generation by evaluating image quality, generation accuracy, computational efficiency, and practical applicability. The results identify their strengths, limitations, and suitability for intelligent digital content creation.

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