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Bi-directional Generation between Attributes and Images

This paper investigates the problem of generating images from visual attributes and vice versa. Given the prevailing research of image recognition, the bi-directional generation between attributes and images is rarely explored due to the challenges of learning a good bidirectionally generative model and the different structure of these two modalities. To address this problem, the bidirectional generative model (BGM) which based on a variant of variational auto-encoders (VAEs) is proposed in this paper. The attributes in BGM are represented by attribute functions. The attribute functions directly ground the meaning of attributes in visual representations. They also allow the BGM to generate images and attributes bi-directionally. The BGM is applied to 3D chairs dataset to verify its validity. The BGM achieves 85.2% and 81.7% accuracy in attribute inference and image reconstruction tasks, respectively. The experimental results demonstrate the efficiency of the BGM.