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Texture Synthesis with Recurrent Variational Auto-Encoder
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We propose a recurrent variational auto-encoder for texture synthesis. A novel loss function, FLTBNK, is used for training the texture synthesizer. It is rotational and partially color invariant loss function. Unlike L2 loss, FLTBNK explicitly models the correlation of color intensity between pixels. Our texture synthesizer generates neighboring tiles to expand a sample texture and is evaluated using various texture patterns from Describable Textures Dataset (DTD). We perform both quantitative and qualitative experiments with various loss functions to evaluate the performance of our proposed loss function (FLTBNK) --- a mini-human subject study is used for the qualitative evaluation.
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Cited by 1 Pith paper
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Texture Image Synthesis Using Spatial GAN Based on Vision Transformers
ViT-SGAN modifies ViTGAN's self-attention with mean-variance and texton descriptors to synthesize textures, but the evaluation is too weak and the equations are too unclear to support the claimed gains.
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