The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
본 논문에서는 두 가지 유형의 사람 이미지 생성을 위한 생성 모델을 제시합니다. 먼저, 이 모델을 포즈 유도 인물 이미지 생성에 적용하고, i.e., 원본 인물 이미지의 질감을 유지하면서 원본 인물 이미지의 포즈를 대상 포즈로 변환합니다. 둘째, 이 모델은 의류 유도 인물 이미지 생성에도 사용되며, i.e., 원본 인물 이미지의 옷 질감을 원하는 옷 질감으로 변경합니다. 제안된 모델의 핵심 아이디어는 다중 스케일 대응을 확립하는 것인데, 이는 포즈 전송으로 인해 발생하는 정렬 불량을 효과적으로 해결하여 외관에 대한 풍부한 정보를 보존할 수 있습니다. 구체적으로 제안된 모델은 두 단계로 구성된다. 1) 생성 과정에서 보다 정확한 안내를 제공하기 위해 먼저 타겟 포즈에 부과된 타겟 의미 맵을 생성한다. 2) 인코더를 통해 다중 스케일 특징 맵을 얻은 후 다중 스케일 대응이 확립되며 이는 세분화된 생성에 유용합니다. 실험 결과는 제안된 방법이 포즈 기반 인물 이미지 생성에 있어 최신 방법보다 우수함을 보여주며, 의복 기반 인물 이미지 생성에서도 그 효율성을 보여줍니다.
Shi-Long SHEN
Harbin Institute of Technology (Shenzhen)
Ai-Guo WU
Harbin Institute of Technology (Shenzhen)
Yong XU
Shenzhen Key Laboratory of Visual Object Detection and Recognition
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부
Shi-Long SHEN, Ai-Guo WU, Yong XU, "Multi-Scale Correspondence Learning for Person Image Generation" in IEICE TRANSACTIONS on Information,
vol. E106-D, no. 5, pp. 804-812, May 2023, doi: 10.1587/transinf.2022DLP0058.
Abstract: A generative model is presented for two types of person image generation in this paper. First, this model is applied to pose-guided person image generation, i.e., converting the pose of a source person image to the target pose while preserving the texture of that source person image. Second, this model is also used for clothing-guided person image generation, i.e., changing the clothing texture of a source person image to the desired clothing texture. The core idea of the proposed model is to establish the multi-scale correspondence, which can effectively address the misalignment introduced by transferring pose, thereby preserving richer information on appearance. Specifically, the proposed model consists of two stages: 1) It first generates the target semantic map imposed on the target pose to provide more accurate guidance during the generation process. 2) After obtaining the multi-scale feature map by the encoder, the multi-scale correspondence is established, which is useful for a fine-grained generation. Experimental results show the proposed method is superior to state-of-the-art methods in pose-guided person image generation and show its effectiveness in clothing-guided person image generation.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2022DLP0058/_p
부
@ARTICLE{e106-d_5_804,
author={Shi-Long SHEN, Ai-Guo WU, Yong XU, },
journal={IEICE TRANSACTIONS on Information},
title={Multi-Scale Correspondence Learning for Person Image Generation},
year={2023},
volume={E106-D},
number={5},
pages={804-812},
abstract={A generative model is presented for two types of person image generation in this paper. First, this model is applied to pose-guided person image generation, i.e., converting the pose of a source person image to the target pose while preserving the texture of that source person image. Second, this model is also used for clothing-guided person image generation, i.e., changing the clothing texture of a source person image to the desired clothing texture. The core idea of the proposed model is to establish the multi-scale correspondence, which can effectively address the misalignment introduced by transferring pose, thereby preserving richer information on appearance. Specifically, the proposed model consists of two stages: 1) It first generates the target semantic map imposed on the target pose to provide more accurate guidance during the generation process. 2) After obtaining the multi-scale feature map by the encoder, the multi-scale correspondence is established, which is useful for a fine-grained generation. Experimental results show the proposed method is superior to state-of-the-art methods in pose-guided person image generation and show its effectiveness in clothing-guided person image generation.},
keywords={},
doi={10.1587/transinf.2022DLP0058},
ISSN={1745-1361},
month={May},}
부
TY - JOUR
TI - Multi-Scale Correspondence Learning for Person Image Generation
T2 - IEICE TRANSACTIONS on Information
SP - 804
EP - 812
AU - Shi-Long SHEN
AU - Ai-Guo WU
AU - Yong XU
PY - 2023
DO - 10.1587/transinf.2022DLP0058
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E106-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2023
AB - A generative model is presented for two types of person image generation in this paper. First, this model is applied to pose-guided person image generation, i.e., converting the pose of a source person image to the target pose while preserving the texture of that source person image. Second, this model is also used for clothing-guided person image generation, i.e., changing the clothing texture of a source person image to the desired clothing texture. The core idea of the proposed model is to establish the multi-scale correspondence, which can effectively address the misalignment introduced by transferring pose, thereby preserving richer information on appearance. Specifically, the proposed model consists of two stages: 1) It first generates the target semantic map imposed on the target pose to provide more accurate guidance during the generation process. 2) After obtaining the multi-scale feature map by the encoder, the multi-scale correspondence is established, which is useful for a fine-grained generation. Experimental results show the proposed method is superior to state-of-the-art methods in pose-guided person image generation and show its effectiveness in clothing-guided person image generation.
ER -