image-generation

Pose-Guided Image Generation

3260 papers • 126 benchmarks • 313 datasets

Pose-guided image generation is the task of generating a new image of a person with guidance from pose information that the new image should synthesise around. ( Image credit: Coordinate-based Texture Inpainting for Pose-Guided Human Image Generation )

(Image credit: Papersgraph)

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Most implemented papers

Coarse-to-Fine Latent Diffusion for Pose-Guided Person Image Synthesis

Yanzuo Lu, Manlin Zhang, Andy J. Ma, Xiaohua Xie, Jian-Huang Lai•Tue Feb 27 2024

To generate more realistic texture details, a hybrid- granularity attention module is proposed to encode multi-scale fine-grained appearance features as bias terms to augment the coarse-grained prompt.

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Coordinate-based Texture Inpainting for Pose-Guided Image Generation

V. Lempitsky, Alexander Vakhitov, A. Grigorev, Artem Sevastopolsky•Tue Nov 27 2018

A new deep learning approach to pose-guided resynthesis of human photographs using a fully-convolutional architecture with deformable skip connections guided by the estimated correspondence field and a new inpainting method that completes the texture of the human body.

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Reposing Humans by Warping 3D Features

B. Leibe, István Sárándi, Markus Knoche•Sun May 31 2020

The problem of reposing an image of a human into any desired novel pose is addressed and a dense feature volume is implicitly learned from human images, which lends itself to simple and intuitive manipulation through explicit geometric warping.

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