This paper presents a comparative evaluation of two state-of-the-art deep-learning-based generative methods, NeuralMaterial and MaterialGAN, for material generation in 3D modeling application – Blender. For comparison, we introduce a baseline method developed using only tools available in Blender. A user study involving 20 participants, including both experts and lay users, was conducted to assess usability and effectiveness. Participants engaged in a material authoring task, evaluating all developed approaches. Usability was evaluated using the System Usability Scale (SUS) questionnaire, and material fidelity was validated by comparing user-created materials to target scene renders using Learned Perceptual Image Patch Similarity (LPIPS) metric. Users are able to achieve the desired results with a similar level of precision as with the existing tools but with higher editability. Results also show preference differences between lay and expert users on which approaches they prefer and why. Our research provides valuable insights into deep-learning-based material generation integrated into everyday workflows. It highlights the importance of considering user preferences and expertise levels when designing such workflows. By comparing performance and usability, we inform the development of more effective and accessible material generation techniques within the field.