MT-GAN: toward realistic image composition based on spatial features

Li, Xiang; Teng, Guowei; An, Ping; Yao, Heng‐Chen

doi:10.1186/s13634-023-01009-y

Cited by 1 publication

(2 citation statements)

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“…Table 2 shows the comparison of the composite results of source object S1. Following MT-GAN [15], we use MSE, PSNR, and the objective estimation score of the compositing images. The objective estimation scores are tested on the PTS and GTS, respectively.…”

Section: Quantitative Experimental Resultsmentioning

confidence: 99%

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Image Composition Method Based on a Spatial Position Analysis Network

Li,

Teng,

et al. 2023

Electronics

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Realistic image composition aims to composite new images by fusing a source object into a target image. It is a challenging problem due to the complex multi-task framework, including sensible object placement, appearance consistency, shadow generation, etc. Most existing researchers attempt to address one of the issues. Especially before compositing, there is no matching assignment between the source object and target image, which often leads to unreasonable results. To address the issues above, we consider image composition as an image generation problem and propose a deep adversarial learning network via spatial position analysis. We target the analysis network segment and classify the objects in target images. A spatial alignment network matches the segmented objects with the source objects, and predicts a sensible placement position, and an adversarial network generates a realistic composite image with the shadow and reflection of the source object. Furthermore, we use the classification information of target objects to filter out unreasonable image compositing. Moreover, we introduce a new test set to evaluate the network generalization for our multi-task image composition dataset. Extensive experimental results of the SHU (Shanghai University) dataset demonstrate that our deep spatial position analysis network remarkably enhances the compositing performance in realistic, shadow, and reflection generations.

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Section: Quantitative Experimental Resultsmentioning

confidence: 99%

“…In addition, there are some studies that address the shadow and reflection [12][13][14] generations of the source object. MT-GAN [15] attempts to utilize image generation methods to simultaneously address object placement, appearance consistency, and shadow generation issues.…”

Section: Introductionmentioning

confidence: 99%