The application discloses to the technical field of
artificial intelligence,
computer vision and
deep learning, and particularly relates to an
image generation quality evaluation and optimization method based on sparse feedback for
industrial design, which comprises the following steps: constructing an
industrial design image dataset containing multi-dimensional expert scores; designing a physical flow
branch to extract material texture and illumination statistical features, and a semantic flow
branch to process picture-text splicing features through a self-attention mechanism; introducing a physical-visual
outer product fusion module to capture second-order feature interaction; using the trained evaluation network as a reward model, combining a sparse sampling strategy and a LoRA fine-tuning mechanism, and guiding the
diffusion model to align with the
industrial design standard under extremely low computing
resource consumption. The application significantly improves the material reality, illumination logic and picture-text consistency of the generated image, and provides an efficient and low-cost solution for automatic
image generation in the field of industrial design.