Soft-Gated Warping-GAN for Pose-Guided Person Image Synthesis

TL;DR

This paper introduces a Soft-Gated Warping-GAN that effectively synthesizes person images under large pose variations by modeling spatial transformations at both structural and feature levels, outperforming existing methods.

Contribution

The paper proposes a novel Warping-GAN with a soft-gated warping-block for better handling geometric variability in pose-guided person image synthesis.

Findings

Outperforms existing methods on large datasets

Effectively models geometric transformations and occlusions

Controls transformation degrees for different target poses

Abstract

Despite remarkable advances in image synthesis research, existing works often fail in manipulating images under the context of large geometric transformations. Synthesizing person images conditioned on arbitrary poses is one of the most representative examples where the generation quality largely relies on the capability of identifying and modeling arbitrary transformations on different body parts. Current generative models are often built on local convolutions and overlook the key challenges (e.g. heavy occlusions, different views or dramatic appearance changes) when distinct geometric changes happen for each part, caused by arbitrary pose manipulations. This paper aims to resolve these challenges induced by geometric variability and spatial displacements via a new Soft-Gated Warping Generative Adversarial Network (Warping-GAN), which is composed of two stages: 1) it first synthesizes a target part segmentation map given a target pose, which depicts the region-level spatial layouts for guiding image synthesis with higher-level structure constraints; 2) the Warping-GAN equipped with a soft-gated warping-block learns feature-level mapping to render textures from the original image into the generated segmentation map. Warping-GAN is capable of controlling different transformation degrees given distinct target poses. Moreover, the proposed warping-block is light-weight and flexible enough to be injected into any networks. Human perceptual studies and quantitative evaluations demonstrate the superiority of our Warping-GAN that significantly outperforms all existing methods on two large datasets.