| Summary: | One of the challenging tasks is to use computer technology to automatically design a virtual indoor scene that both satisfies realness and matches the target emotion. The subjective nature of emotions brings uncertainty of results. At present, there is a lack of approach to identify and evaluate emotion of indoor scenes. In addition, under the premise of fully considering emotional appeals, the authenticity of scene is also one of important factors in indoor scene design. Aiming at above problems, a novel optimization algorithm combining Chinese elements for indoor scenes rendering is proposed. Firstly, an emotion classifier is trained to identify and evaluate the emotion with the features extracted via deep learning from a indoor scene dataset containing 25000 images. Secondly, in order to ensure the authenticity of rendering results, an algorithm is proposed to evaluate how realistic the colors of the objects’ textures. Next, an algorithm is designed to render indoor scene automatically according to the target emotion. Then, a style transfer algorithm integrating with Chinese elements is used to carry out fine-grained refinement processing on the furnishings in an indoor scene, improve the spatial connotation, cultural connotation and emotional expression of rendering results, and enhance the visual appeal. Finally, the approach is tested in four indoor scenes, and the correctness and effectiveness of the approach are verified through statistical analysis of results and user survey data.
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