VGFlow: Visibility guided Flow Network for Human Reposing
This work addresses the challenge of human reposing for applications in graphics and AI, offering incremental improvements over existing methods.
The paper tackles the problem of generating realistic images of people in arbitrary poses by proposing VGFlow, a model that uses a visibility-guided flow module and a self-supervised patch-wise loss to preserve texture and handle occlusions, achieving state-of-the-art results on metrics like SSIM, LPIPS, and FID.
The task of human reposing involves generating a realistic image of a person standing in an arbitrary conceivable pose. There are multiple difficulties in generating perceptually accurate images, and existing methods suffer from limitations in preserving texture, maintaining pattern coherence, respecting cloth boundaries, handling occlusions, manipulating skin generation, etc. These difficulties are further exacerbated by the fact that the possible space of pose orientation for humans is large and variable, the nature of clothing items is highly non-rigid, and the diversity in body shape differs largely among the population. To alleviate these difficulties and synthesize perceptually accurate images, we propose VGFlow. Our model uses a visibility-guided flow module to disentangle the flow into visible and invisible parts of the target for simultaneous texture preservation and style manipulation. Furthermore, to tackle distinct body shapes and avoid network artifacts, we also incorporate a self-supervised patch-wise "realness" loss to improve the output. VGFlow achieves state-of-the-art results as observed qualitatively and quantitatively on different image quality metrics (SSIM, LPIPS, FID).