We synthesize comprehensive-entire body human images commencing from a provided human pose with two committed methods. 1) With some texts describing the shapes of garments, the provided human pose is initially translated to some human parsing map. 2) The ultimate human graphic is then generated by furnishing the method with much more characteristics regarding the textures of garments. Specially, to model the variety of clothes textures, we create a hierarchical texture-aware codebook that outlets multi-scale neural representations for each kind of texture. The codebook at the coarse amount includes the structural representations of textures, although the codebook at the high-quality level concentrates on the small print of textures. To make full use of the realized hierarchical codebook to synthesize preferred images, a diffusionbased transformer sampler with combination of professionals is To begin with used to sample indices from the coarsest degree of the codebook, which then is used to predict the indices of your codebook at finer stages. The predicted indices at various levels are translated to human photographs with the decoder figured out accompanied with hierarchical codebooks. Using mixture-of-professionals allows for the generated impression conditioned around the good-grained text enter. The prediction for finer level indices refines the standard of clothing textures. Intensive quantitative and qualitative evaluations display that our proposed Text2Human framework can make far more numerous and reasonable human images when compared with point out-of-the-art methods. Hyperlinks

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