Search Results for author: Zhixin Shu

Found 20 papers, 5 papers with code

PaletteNeRF: Palette-based Appearance Editing of Neural Radiance Fields

no code implementations21 Dec 2022 Zhengfei Kuang, Fujun Luan, Sai Bi, Zhixin Shu, Gordon Wetzstein, Kalyan Sunkavalli

Recent advances in neural radiance fields have enabled the high-fidelity 3D reconstruction of complex scenes for novel view synthesis.

3D Reconstruction Novel View Synthesis

3D-FM GAN: Towards 3D-Controllable Face Manipulation

no code implementations24 Aug 2022 Yuchen Liu, Zhixin Shu, Yijun Li, Zhe Lin, Richard Zhang, S. Y. Kung

While concatenating GAN inversion and a 3D-aware, noise-to-image GAN is a straight-forward solution, it is inefficient and may lead to noticeable drop in editing quality.

RigNeRF: Fully Controllable Neural 3D Portraits

no code implementations CVPR 2022 ShahRukh Athar, Zexiang Xu, Kalyan Sunkavalli, Eli Shechtman, Zhixin Shu

In this work, we propose RigNeRF, a system that goes beyond just novel view synthesis and enables full control of head pose and facial expressions learned from a single portrait video.

Face Model Neural Rendering +1

Learning Motion-Dependent Appearance for High-Fidelity Rendering of Dynamic Humans from a Single Camera

no code implementations CVPR 2022 Jae Shin Yoon, Duygu Ceylan, Tuanfeng Y. Wang, Jingwan Lu, Jimei Yang, Zhixin Shu, Hyun Soo Park

Appearance of dressed humans undergoes a complex geometric transformation induced not only by the static pose but also by its dynamics, i. e., there exists a number of cloth geometric configurations given a pose depending on the way it has moved.

Point-NeRF: Point-based Neural Radiance Fields

1 code implementation CVPR 2022 Qiangeng Xu, Zexiang Xu, Julien Philip, Sai Bi, Zhixin Shu, Kalyan Sunkavalli, Ulrich Neumann

Point-NeRF combines the advantages of these two approaches by using neural 3D point clouds, with associated neural features, to model a radiance field.

3D Reconstruction Neural Rendering

FLAME-in-NeRF: Neural control of Radiance Fields for Free View Face Animation

no code implementations29 Sep 2021 ShahRukh Athar, Zhixin Shu, Dimitris Samaras

In this work, we design a system that enables 1) novel view synthesis for portrait video, of both the human subject and the scene they are in and 2) explicit control of the facial expressions through a low-dimensional expression representation.

Neural Rendering Novel View Synthesis

Learning Surface Parameterization for Document Image Unwarping

no code implementations29 Sep 2021 Sagnik Das, Ke Ma, Zhixin Shu, Dimitris Samaras

We also demonstrate the usefulness of our system by applying it to document texture editing.

3D Scene Reconstruction

FLAME-in-NeRF : Neural control of Radiance Fields for Free View Face Animation

no code implementations10 Aug 2021 ShahRukh Athar, Zhixin Shu, Dimitris Samaras

In this work, we design a system that enables both novel view synthesis for portrait video, including the human subject and the scene background, and explicit control of the facial expressions through a low-dimensional expression representation.

Face Model Neural Rendering +1

Single-image Full-body Human Relighting

no code implementations15 Jul 2021 Manuel Lagunas, Xin Sun, Jimei Yang, Ruben Villegas, Jianming Zhang, Zhixin Shu, Belen Masia, Diego Gutierrez

We present a single-image data-driven method to automatically relight images with full-body humans in them.

Image Reconstruction

Content-Aware GAN Compression

1 code implementation CVPR 2021 Yuchen Liu, Zhixin Shu, Yijun Li, Zhe Lin, Federico Perazzi, S. Y. Kung

We then propose a novel content-aware method to guide the processes of both pruning and distillation.

Image Generation Image Manipulation +1

Learning Clusterable Visual Features for Zero-Shot Recognition

no code implementations7 Oct 2020 Jingyi Xu, Zhixin Shu, Dimitris Samaras

However, some testing data are considered "hard" as they lie close to the decision boundaries and are prone to misclassification, leading to performance degradation for ZSL.

Classification Few-Shot Learning +2

Self-supervised Deformation Modeling for Facial Expression Editing

no code implementations2 Nov 2019 ShahRukh Athar, Zhixin Shu, Dimitris Samaras

In the "motion-editing" step, we explicitly model facial movement through image deformation, warping the image into the desired expression.

Disentanglement Facial Editing +2

Latent Space Optimal Transport for Generative Models

no code implementations16 Sep 2018 Huidong Liu, Yang Guo, Na lei, Zhixin Shu, Shing-Tung Yau, Dimitris Samaras, Xianfeng GU

Experimental results on an eight-Gaussian dataset show that the proposed OT can handle multi-cluster distributions.

DocUNet: Document Image Unwarping via a Stacked U-Net

1 code implementation CVPR 2018 Ke Ma, Zhixin Shu, Xue Bai, Jue Wang, Dimitris Samaras

The network is trained on this dataset with various data augmentations to improve its generalization ability.

Ranked #3 on Local Distortion on DocUNet (using extra training data)

Local Distortion MS-SSIM +1

An Adversarial Neuro-Tensorial Approach For Learning Disentangled Representations

no code implementations28 Nov 2017 Mengjiao Wang, Zhixin Shu, Shiyang Cheng, Yannis Panagakis, Dimitris Samaras, Stefanos Zafeiriou

Several factors contribute to the appearance of an object in a visual scene, including pose, illumination, and deformation, among others.

3D Face Reconstruction

Improving Heterogeneous Face Recognition with Conditional Adversarial Networks

no code implementations8 Sep 2017 Wuming Zhang, Zhixin Shu, Dimitris Samaras, Liming Chen

Heterogeneous face recognition between color image and depth image is a much desired capacity for real world applications where shape information is looked upon as merely involved in gallery.

Face Recognition Heterogeneous Face Recognition +1

Neural Face Editing with Intrinsic Image Disentangling

2 code implementations CVPR 2017 Zhixin Shu, Ersin Yumer, Sunil Hadap, Kalyan Sunkavalli, Eli Shechtman, Dimitris Samaras

Traditional face editing methods often require a number of sophisticated and task specific algorithms to be applied one after the other --- a process that is tedious, fragile, and computationally intensive.

Facial Editing

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