Deformable GANs for Pose-Based Human Image Generation

In this paper we address the problem of generating person images conditioned on a given pose. Specifically, given an image of a person and a target pose, we synthesize a new image of that person in the novel pose. In order to deal with pixel-to-pixel misalignments caused by the pose differences, we...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition s. 3408 - 3416
Hlavní autoři: Siarohin, Aliaksandr, Sangineto, Enver, Lathuiliere, Stephane, Sebe, Nicu
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.06.2018
Témata:
Computer architecture
Decoding
Gallium nitride
Generators
Strain
Task analysis
ISSN:1063-6919
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:In this paper we address the problem of generating person images conditioned on a given pose. Specifically, given an image of a person and a target pose, we synthesize a new image of that person in the novel pose. In order to deal with pixel-to-pixel misalignments caused by the pose differences, we introduce deformable skip connections in the generator of our Generative Adversarial Network. Moreover, a nearest-neighbour loss is proposed instead of the common L1 and L2 losses in order to match the details of the generated image with the target image. We test our approach using photos of persons in different poses and we compare our method with previous work in this area showing state-of-the-art results in two benchmarks. Our method can be applied to the wider field of deformable object generation, provided that the pose of the articulated object can be extracted using a keypoint detector.
ISSN:1063-6919
DOI:10.1109/CVPR.2018.00359