Search Results for author: Pengfei Wan

Found 16 papers, 8 papers with code

Multi-Modal Face Stylization with a Generative Prior

no code implementations29 May 2023 Mengtian Li, Yi Dong, Minxuan Lin, Haibin Huang, Pengfei Wan, Chongyang Ma

We also introduce a two-stage training strategy, where we train the encoder in the first stage to align the feature maps with StyleGAN and enable a faithful reconstruction of input faces.

Face Generation

Bridging CLIP and StyleGAN through Latent Alignment for Image Editing

no code implementations10 Oct 2022 Wanfeng Zheng, Qiang Li, Xiaoyan Guo, Pengfei Wan, Zhongyuan Wang

More specifically, our efforts consist of three parts: 1) a data-free training strategy to train latent mappers to bridge the latent space of CLIP and StyleGAN; 2) for more precise mapping, temporal relative consistency is proposed to address the knowledge distribution bias problem among different latent spaces; 3) to refine the mapped latent in s space, adaptive style mixing is also proposed.

Image Manipulation Language Modelling +1

ITTR: Unpaired Image-to-Image Translation with Transformers

no code implementations30 Mar 2022 Wanfeng Zheng, Qiang Li, Guoxin Zhang, Pengfei Wan, Zhongyuan Wang

Unpaired image-to-image translation is to translate an image from a source domain to a target domain without paired training data.

Image-to-Image Translation Translation

Wavelet Knowledge Distillation: Towards Efficient Image-to-Image Translation

no code implementations CVPR 2022 Linfeng Zhang, Xin Chen, Xiaobing Tu, Pengfei Wan, Ning Xu, Kaisheng Ma

Instead of directly distilling the generated images of teachers, wavelet knowledge distillation first decomposes the images into different frequency bands with discrete wavelet transformation and then only distills the high frequency bands.

Image-to-Image Translation Knowledge Distillation +1

PMP-Net++: Point Cloud Completion by Transformer-Enhanced Multi-step Point Moving Paths

1 code implementation19 Feb 2022 Xin Wen, Peng Xiang, Zhizhong Han, Yan-Pei Cao, Pengfei Wan, Wen Zheng, Yu-Shen Liu

It moves each point of incomplete input to obtain a complete point cloud, where total distance of point moving paths (PMPs) should be the shortest.

Point Cloud Completion Representation Learning

Snowflake Point Deconvolution for Point Cloud Completion and Generation with Skip-Transformer

1 code implementation18 Feb 2022 Peng Xiang, Xin Wen, Yu-Shen Liu, Yan-Pei Cao, Pengfei Wan, Wen Zheng, Zhizhong Han

Our insight into the detailed geometry is to introduce a skip-transformer in the SPD to learn the point splitting patterns that can best fit the local regions.

Image Reconstruction Point Cloud Completion

Assessing a Single Image in Reference-Guided Image Synthesis

no code implementations8 Dec 2021 Jiayi Guo, Chaoqun Du, Jiangshan Wang, Huijuan Huang, Pengfei Wan, Gao Huang

For Reference-guided Image Synthesis (RIS) tasks, i. e., rendering a source image in the style of another reference image, where assessing the quality of a single generated image is crucial, these metrics are not applicable.

Image Generation

BlendGAN: Implicitly GAN Blending for Arbitrary Stylized Face Generation

3 code implementations NeurIPS 2021 Mingcong Liu, Qiang Li, Zekui Qin, Guoxin Zhang, Pengfei Wan, Wen Zheng

Specifically, we first train a self-supervised style encoder on the generic artistic dataset to extract the representations of arbitrary styles.

Face Generation

SnowflakeNet: Point Cloud Completion by Snowflake Point Deconvolution with Skip-Transformer

2 code implementations ICCV 2021 Peng Xiang, Xin Wen, Yu-Shen Liu, Yan-Pei Cao, Pengfei Wan, Wen Zheng, Zhizhong Han

However, previous methods usually suffered from discrete nature of point cloud and unstructured prediction of points in local regions, which makes it hard to reveal fine local geometric details on the complete shape.

Point Cloud Completion

Exploring Set Similarity for Dense Self-supervised Representation Learning

no code implementations CVPR 2022 Zhaoqing Wang, Qiang Li, Guoxin Zhang, Pengfei Wan, Wen Zheng, Nannan Wang, Mingming Gong, Tongliang Liu

By considering the spatial correspondence, dense self-supervised representation learning has achieved superior performance on various dense prediction tasks.

Instance Segmentation Keypoint Detection +4

Cycle4Completion: Unpaired Point Cloud Completion using Cycle Transformation with Missing Region Coding

1 code implementation CVPR 2021 Xin Wen, Zhizhong Han, Yan-Pei Cao, Pengfei Wan, Wen Zheng, Yu-Shen Liu

We provide a comprehensive evaluation in experiments, which shows that our model with the learned bidirectional geometry correspondence outperforms state-of-the-art unpaired completion methods.

Point Cloud Completion

Camera-Space Hand Mesh Recovery via Semantic Aggregation and Adaptive 2D-1D Registration

1 code implementation CVPR 2021 Xingyu Chen, Yufeng Liu, Chongyang Ma, Jianlong Chang, Huayan Wang, Tian Chen, Xiaoyan Guo, Pengfei Wan, Wen Zheng

In the root-relative mesh recovery task, we exploit semantic relations among joints to generate a 3D mesh from the extracted 2D cues.

PMP-Net: Point Cloud Completion by Learning Multi-step Point Moving Paths

1 code implementation CVPR 2021 Xin Wen, Peng Xiang, Zhizhong Han, Yan-Pei Cao, Pengfei Wan, Wen Zheng, Yu-Shen Liu

As a result, the network learns a strict and unique correspondence on point-level, which can capture the detailed topology and structure relationships between the incomplete shape and the complete target, and thus improves the quality of the predicted complete shape.

Point Cloud Completion

Precision Enhancement of 3D Surfaces from Multiple Compressed Depth Maps

no code implementations25 Feb 2014 Pengfei Wan, Gene Cheung, Philip A. Chou, Dinei Florencio, Cha Zhang, Oscar C. Au

In texture-plus-depth representation of a 3D scene, depth maps from different camera viewpoints are typically lossily compressed via the classical transform coding / coefficient quantization paradigm.


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