Search Results for author: Changgong Zhang

Found 13 papers, 2 papers with code

Auto-regressive Image Synthesis with Integrated Quantization

no code implementations21 Jul 2022 Fangneng Zhan, Yingchen Yu, Rongliang Wu, Jiahui Zhang, Kaiwen Cui, Changgong Zhang, Shijian Lu

Extensive experiments over multiple conditional image generation tasks show that our method achieves superior diverse image generation performance qualitatively and quantitatively as compared with the state-of-the-art.

Conditional Image Generation Inductive Bias +1

Marginal Contrastive Correspondence for Guided Image Generation

no code implementations CVPR 2022 Fangneng Zhan, Yingchen Yu, Rongliang Wu, Jiahui Zhang, Shijian Lu, Changgong Zhang

We design a Marginal Contrastive Learning Network (MCL-Net) that explores contrastive learning to learn domain-invariant features for realistic exemplar-based image translation.

Contrastive Learning Image Generation +2

Conditional Directed Graph Convolution for 3D Human Pose Estimation

1 code implementation16 Jul 2021 WenBo Hu, Changgong Zhang, Fangneng Zhan, Lei Zhang, Tien-Tsin Wong

Based on this representation, we further propose a spatial-temporal conditional directed graph convolution to leverage varying non-local dependence for different poses by conditioning the graph topology on input poses.

3D Human Pose Estimation

Sparse Needlets for Lighting Estimation with Spherical Transport Loss

no code implementations ICCV 2021 Fangneng Zhan, Changgong Zhang, WenBo Hu, Shijian Lu, Feiying Ma, Xuansong Xie, Ling Shao

Accurate lighting estimation is challenging yet critical to many computer vision and computer graphics tasks such as high-dynamic-range (HDR) relighting.

Lighting Estimation

GMLight: Lighting Estimation via Geometric Distribution Approximation

1 code implementation20 Feb 2021 Fangneng Zhan, Yingchen Yu, Changgong Zhang, Rongliang Wu, WenBo Hu, Shijian Lu, Feiying Ma, Xuansong Xie, Ling Shao

This paper presents Geometric Mover's Light (GMLight), a lighting estimation framework that employs a regression network and a generative projector for effective illumination estimation.

Lighting Estimation regression

EMLight: Lighting Estimation via Spherical Distribution Approximation

no code implementations21 Dec 2020 Fangneng Zhan, Changgong Zhang, Yingchen Yu, Yuan Chang, Shijian Lu, Feiying Ma, Xuansong Xie

Motivated by the Earth Mover distance, we design a novel spherical mover's loss that guides to regress light distribution parameters accurately by taking advantage of the subtleties of spherical distribution.

Lighting Estimation regression

Adversarial Image Composition with Auxiliary Illumination

no code implementations17 Sep 2020 Fangneng Zhan, Shijian Lu, Changgong Zhang, Feiying Ma, Xuansong Xie

State-of-the-art methods strive to harmonize the composed image by adapting the style of foreground objects to be compatible with the background image, whereas the potential shadow of foreground objects within the composed image which is critical to the composition realism is largely neglected.

Towards Realistic 3D Embedding via View Alignment

no code implementations14 Jul 2020 Changgong Zhang, Fangneng Zhan, Shijian Lu, Feiying Ma, Xuansong Xie

Recent advances in generative adversarial networks (GANs) have achieved great success in automated image composition that generates new images by embedding interested foreground objects into background images automatically.

Spatial-Aware GAN for Unsupervised Person Re-identification

no code implementations26 Nov 2019 Changgong Zhang, Fangneng Zhan

The recent person re-identification research has achieved great success by learning from a large number of labeled person images.

Unsupervised Domain Adaptation Unsupervised Person Re-Identification

Scene Text Synthesis for Efficient and Effective Deep Network Training

no code implementations26 Jan 2019 Changgong Zhang, Fangneng Zhan, Hongyuan Zhu, Shijian Lu

Experiments over a number of public datasets demonstrate the effectiveness of our proposed image synthesis technique - the use of our synthesized images in deep network training is capable of achieving similar or even better scene text detection and scene text recognition performance as compared with using real images.

Image Generation Scene Text Detection +1

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