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 L 1 and L 2 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.

Deformable GANs for Pose-Based Human Image Generation / Siarohin, Aliaksandr; Sangineto, Enver; Lathuiliere, Stephane; Sebe, Nicu. - (2018), pp. 3408-3416. (Intervento presentato al convegno 31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018 tenutosi a Salt Lake City nel 18-23 June 2018) [10.1109/CVPR.2018.00359].

Deformable GANs for Pose-Based Human Image Generation

Sangineto, Enver;Sebe, Nicu
2018

Abstract

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 L 1 and L 2 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.
2018
31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018
Salt Lake City
18-23 June 2018
3408
3416
Siarohin, Aliaksandr; Sangineto, Enver; Lathuiliere, Stephane; Sebe, Nicu
Deformable GANs for Pose-Based Human Image Generation / Siarohin, Aliaksandr; Sangineto, Enver; Lathuiliere, Stephane; Sebe, Nicu. - (2018), pp. 3408-3416. (Intervento presentato al convegno 31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018 tenutosi a Salt Lake City nel 18-23 June 2018) [10.1109/CVPR.2018.00359].
File in questo prodotto:
Non ci sono file associati a questo prodotto.
Pubblicazioni consigliate

Licenza Creative Commons
I metadati presenti in IRIS UNIMORE sono rilasciati con licenza Creative Commons CC0 1.0 Universal, mentre i file delle pubblicazioni sono rilasciati con licenza Attribuzione 4.0 Internazionale (CC BY 4.0), salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1264592
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 365
  • ???jsp.display-item.citation.isi??? 282
social impact