Search Results for author: Yinghao Xu

Found 31 papers, 15 papers with code

3D generation on ImageNet

no code implementations2 Mar 2023 Ivan Skorokhodov, Aliaksandr Siarohin, Yinghao Xu, Jian Ren, Hsin-Ying Lee, Peter Wonka, Sergey Tulyakov

Existing 3D-from-2D generators are typically designed for well-curated single-category datasets, where all the objects have (approximately) the same scale, 3D location, and orientation, and the camera always points to the center of the scene.

Learning 3D-aware Image Synthesis with Unknown Pose Distribution

no code implementations CVPR 2023 Zifan Shi, Yujun Shen, Yinghao Xu, Sida Peng, Yiyi Liao, Sheng Guo, Qifeng Chen, Dit-yan Yeung

Existing methods for 3D-aware image synthesis largely depend on the 3D pose distribution pre-estimated on the training set.

3D-Aware Image Synthesis

GH-Feat: Learning Versatile Generative Hierarchical Features from GANs

no code implementations12 Jan 2023 Yinghao Xu, Yujun Shen, Jiapeng Zhu, Ceyuan Yang, Bolei Zhou

In this work we investigate that such a generative feature learned from image synthesis exhibits great potentials in solving a wide range of computer vision tasks, including both generative ones and more importantly discriminative ones.

Face Verification Image Harmonization +3

DisCoScene: Spatially Disentangled Generative Radiance Fields for Controllable 3D-aware Scene Synthesis

no code implementations CVPR 2023 Yinghao Xu, Menglei Chai, Zifan Shi, Sida Peng, Ivan Skorokhodov, Aliaksandr Siarohin, Ceyuan Yang, Yujun Shen, Hsin-Ying Lee, Bolei Zhou, Sergey Tulyakov

Existing 3D-aware image synthesis approaches mainly focus on generating a single canonical object and show limited capacity in composing a complex scene containing a variety of objects.

3D-Aware Image Synthesis

Towards Smooth Video Composition

1 code implementation14 Dec 2022 Qihang Zhang, Ceyuan Yang, Yujun Shen, Yinghao Xu, Bolei Zhou

Video generation requires synthesizing consistent and persistent frames with dynamic content over time.

Image Generation single-image-generation +2

GLeaD: Improving GANs with A Generator-Leading Task

1 code implementation CVPR 2023 Qingyan Bai, Ceyuan Yang, Yinghao Xu, Xihui Liu, Yujiu Yang, Yujun Shen

Generative adversarial network (GAN) is formulated as a two-player game between a generator (G) and a discriminator (D), where D is asked to differentiate whether an image comes from real data or is produced by G. Under such a formulation, D plays as the rule maker and hence tends to dominate the competition.

Deep Generative Models on 3D Representations: A Survey

1 code implementation27 Oct 2022 Zifan Shi, Sida Peng, Yinghao Xu, Yiyi Liao, Yujun Shen

Generative models, as an important family of statistical modeling, target learning the observed data distribution via generating new instances.

3D-Aware Image Synthesis 3D Shape Generation

Improving 3D-aware Image Synthesis with A Geometry-aware Discriminator

no code implementations30 Sep 2022 Zifan Shi, Yinghao Xu, Yujun Shen, Deli Zhao, Qifeng Chen, Dit-yan Yeung

We argue that, considering the two-player game in the formulation of GANs, only making the generator 3D-aware is not enough.

3D-Aware Image Synthesis Novel View Synthesis

Improving GANs with A Dynamic Discriminator

no code implementations20 Sep 2022 Ceyuan Yang, Yujun Shen, Yinghao Xu, Deli Zhao, Bo Dai, Bolei Zhou

Two capacity adjusting schemes are developed for training GANs under different data regimes: i) given a sufficient amount of training data, the discriminator benefits from a progressively increased learning capacity, and ii) when the training data is limited, gradually decreasing the layer width mitigates the over-fitting issue of the discriminator.

3D-Aware Image Synthesis Data Augmentation

High-fidelity GAN Inversion with Padding Space

1 code implementation21 Mar 2022 Qingyan Bai, Yinghao Xu, Jiapeng Zhu, Weihao Xia, Yujiu Yang, Yujun Shen

In this work, we propose to involve the padding space of the generator to complement the latent space with spatial information.

Image Manipulation Inductive Bias +1

Region-Based Semantic Factorization in GANs

1 code implementation19 Feb 2022 Jiapeng Zhu, Yujun Shen, Yinghao Xu, Deli Zhao, Qifeng Chen

Despite the rapid advancement of semantic discovery in the latent space of Generative Adversarial Networks (GANs), existing approaches either are limited to finding global attributes or rely on a number of segmentation masks to identify local attributes.

Semantic-Aware Implicit Neural Audio-Driven Video Portrait Generation

no code implementations19 Jan 2022 Xian Liu, Yinghao Xu, Qianyi Wu, Hang Zhou, Wayne Wu, Bolei Zhou

Moreover, to enable portrait rendering in one unified neural radiance field, a Torso Deformation module is designed to stabilize the large-scale non-rigid torso motions.

3D-aware Image Synthesis via Learning Structural and Textural Representations

1 code implementation CVPR 2022 Yinghao Xu, Sida Peng, Ceyuan Yang, Yujun Shen, Bolei Zhou

The feature field is further accumulated into a 2D feature map as the textural representation, followed by a neural renderer for appearance synthesis.

3D-Aware Image Synthesis

Cross-Model Pseudo-Labeling for Semi-Supervised Action Recognition

no code implementations CVPR 2022 Yinghao Xu, Fangyun Wei, Xiao Sun, Ceyuan Yang, Yujun Shen, Bo Dai, Bolei Zhou, Stephen Lin

Typically in recent work, the pseudo-labels are obtained by training a model on the labeled data, and then using confident predictions from the model to teach itself.

Action Recognition

Improving GAN Equilibrium by Raising Spatial Awareness

1 code implementation CVPR 2022 Jianyuan Wang, Ceyuan Yang, Yinghao Xu, Yujun Shen, Hongdong Li, Bolei Zhou

We further propose to align the spatial awareness of G with the attention map induced from D. Through this way we effectively lessen the information gap between D and G. Extensive results show that our method pushes the two-player game in GANs closer to the equilibrium, leading to a better synthesis performance.

Inductive Bias

One-Shot Generative Domain Adaptation

no code implementations18 Nov 2021 Ceyuan Yang, Yujun Shen, Zhiyi Zhang, Yinghao Xu, Jiapeng Zhu, Zhirong Wu, Bolei Zhou

We then equip the well-learned discriminator backbone with an attribute classifier to ensure that the generator captures the appropriate characters from the reference.

Domain Adaptation

Improving Out-of-Distribution Robustness of Classifiers Through Interpolated Generative Models

no code implementations29 Sep 2021 Haoyue Bai, Ceyuan Yang, Yinghao Xu, S.-H. Gary Chan, Bolei Zhou

In this paper, we employ interpolated generative models to generate OoD samples at training time via data augmentation.

Data Augmentation

Learning Object-Compositional Neural Radiance Field for Editable Scene Rendering

no code implementations ICCV 2021 Bangbang Yang, yinda zhang, Yinghao Xu, Yijin Li, Han Zhou, Hujun Bao, Guofeng Zhang, Zhaopeng Cui

In this paper, we present a novel neural scene rendering system, which learns an object-compositional neural radiance field and produces realistic rendering with editing capability for a clustered and real-world scene.

Neural Rendering Novel View Synthesis

CompConv: A Compact Convolution Module for Efficient Feature Learning

no code implementations19 Jun 2021 Chen Zhang, Yinghao Xu, Yujun Shen

Convolutional Neural Networks (CNNs) have achieved remarkable success in various computer vision tasks but rely on tremendous computational cost.

Data-Efficient Instance Generation from Instance Discrimination

1 code implementation NeurIPS 2021 Ceyuan Yang, Yujun Shen, Yinghao Xu, Bolei Zhou

Meanwhile, the learned instance discrimination capability from the discriminator is in turn exploited to encourage the generator for diverse generation.

Data Augmentation Image Generation

Neural Body: Implicit Neural Representations with Structured Latent Codes for Novel View Synthesis of Dynamic Humans

3 code implementations CVPR 2021 Sida Peng, Yuanqing Zhang, Yinghao Xu, Qianqian Wang, Qing Shuai, Hujun Bao, Xiaowei Zhou

To this end, we propose Neural Body, a new human body representation which assumes that the learned neural representations at different frames share the same set of latent codes anchored to a deformable mesh, so that the observations across frames can be naturally integrated.

Novel View Synthesis Representation Learning

Generative Hierarchical Features from Synthesizing Images

1 code implementation CVPR 2021 Yinghao Xu, Yujun Shen, Jiapeng Zhu, Ceyuan Yang, Bolei Zhou

Generative Adversarial Networks (GANs) have recently advanced image synthesis by learning the underlying distribution of the observed data.

Face Verification Image Classification +2

Unsupervised Landmark Learning from Unpaired Data

1 code implementation29 Jun 2020 Yinghao Xu, Ceyuan Yang, Ziwei Liu, Bo Dai, Bolei Zhou

Recent attempts for unsupervised landmark learning leverage synthesized image pairs that are similar in appearance but different in poses.

Video Representation Learning with Visual Tempo Consistency

1 code implementation28 Jun 2020 Ceyuan Yang, Yinghao Xu, Bo Dai, Bolei Zhou

Visual tempo, which describes how fast an action goes, has shown its potential in supervised action recognition.

Action Anticipation Action Detection +3

Temporal Pyramid Network for Action Recognition

3 code implementations CVPR 2020 Ceyuan Yang, Yinghao Xu, Jianping Shi, Bo Dai, Bolei Zhou

Previous works often capture the visual tempo through sampling raw videos at multiple rates and constructing an input-level frame pyramid, which usually requires a costly multi-branch network to handle.

Action Recognition

Dense RepPoints: Representing Visual Objects with Dense Point Sets

2 code implementations ECCV 2020 Ze Yang, Yinghao Xu, Han Xue, Zheng Zhang, Raquel Urtasun, Li-Wei Wang, Stephen Lin, Han Hu

We present a new object representation, called Dense RepPoints, that utilizes a large set of points to describe an object at multiple levels, including both box level and pixel level.

Object Detection

A Main/Subsidiary Network Framework for Simplifying Binary Neural Networks

no code implementations CVPR 2019 Yinghao Xu, Xin Dong, Yudian Li, Hao Su

To reduce memory footprint and run-time latency, techniques such as neural net-work pruning and binarization have been explored separately.

Binarization Image Classification

A Main/Subsidiary Network Framework for Simplifying Binary Neural Network

no code implementations11 Dec 2018 Yinghao Xu, Xin Dong, Yudian Li, Hao Su

To reduce memory footprint and run-time latency, techniques such as neural network pruning and binarization have been explored separately.

Binarization Image Classification +1

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