Down to the Last Detail: Virtual Try-on with Detail Carving
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Virtual try-on under arbitrary poses has attracted lots of research attention due to its huge potential applications. However, existing methods can hardly preserve the details in clothing texture and facial identity (face, hair) while fitting novel clothes and poses onto a person. In this paper, we propose a novel multi-stage framework to synthesize person images, where rich details in salient regions can be well preserved. Specifically, a multi-stage framework is proposed to decompose the generation into spatial alignment followed by a coarse-to-fine generation. To better preserve the details in salient areas such as clothing and facial areas, we propose a Tree-Block (tree dilated fusion block) to harness multi-scale features in the generator networks. With end-to-end training of multiple stages, the whole framework can be jointly optimized for results with significantly better visual fidelity and richer details. Extensive experiments on standard datasets demonstrate that our proposed framework achieves the state-of-the-art performance, especially in preserving the visual details in clothing texture and facial identity. Our implementation will be publicly available soon.
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