Search Results for author: Hao Ouyang

Found 12 papers, 9 papers with code

Real-time 3D-aware Portrait Editing from a Single Image

no code implementations21 Feb 2024 Qingyan Bai, Yinghao Xu, Zifan Shi, Hao Ouyang, Qiuyu Wang, Ceyuan Yang, Xuan Wang, Gordon Wetzstein, Yujun Shen, Qifeng Chen

This work presents 3DPE, a practical tool that can efficiently edit a face image following given prompts, like reference images or text descriptions, in the 3D-aware manner.

Text2Immersion: Generative Immersive Scene with 3D Gaussians

no code implementations14 Dec 2023 Hao Ouyang, Kathryn Heal, Stephen Lombardi, Tiancheng Sun

We introduce Text2Immersion, an elegant method for producing high-quality 3D immersive scenes from text prompts.

Depth Estimation Scene Generation

Learning Naturally Aggregated Appearance for Efficient 3D Editing

1 code implementation11 Dec 2023 Ka Leong Cheng, Qiuyu Wang, Zifan Shi, Kecheng Zheng, Yinghao Xu, Hao Ouyang, Qifeng Chen, Yujun Shen

Neural radiance fields, which represent a 3D scene as a color field and a density field, have demonstrated great progress in novel view synthesis yet are unfavorable for editing due to the implicitness.

Novel View Synthesis

CoDeF: Content Deformation Fields for Temporally Consistent Video Processing

1 code implementation15 Aug 2023 Hao Ouyang, Qiuyu Wang, Yuxi Xiao, Qingyan Bai, Juntao Zhang, Kecheng Zheng, Xiaowei Zhou, Qifeng Chen, Yujun Shen

We present the content deformation field CoDeF as a new type of video representation, which consists of a canonical content field aggregating the static contents in the entire video and a temporal deformation field recording the transformations from the canonical image (i. e., rendered from the canonical content field) to each individual frame along the time axis. Given a target video, these two fields are jointly optimized to reconstruct it through a carefully tailored rendering pipeline. We advisedly introduce some regularizations into the optimization process, urging the canonical content field to inherit semantics (e. g., the object shape) from the video. With such a design, CoDeF naturally supports lifting image algorithms for video processing, in the sense that one can apply an image algorithm to the canonical image and effortlessly propagate the outcomes to the entire video with the aid of the temporal deformation field. We experimentally show that CoDeF is able to lift image-to-image translation to video-to-video translation and lift keypoint detection to keypoint tracking without any training. More importantly, thanks to our lifting strategy that deploys the algorithms on only one image, we achieve superior cross-frame consistency in processed videos compared to existing video-to-video translation approaches, and even manage to track non-rigid objects like water and smog. Project page can be found at https://qiuyu96. github. io/CoDeF/.

Image-to-Image Translation Keypoint Detection +1

High-fidelity 3D GAN Inversion by Pseudo-multi-view Optimization

1 code implementation CVPR 2023 Jiaxin Xie, Hao Ouyang, Jingtan Piao, Chenyang Lei, Qifeng Chen

We present a high-fidelity 3D generative adversarial network (GAN) inversion framework that can synthesize photo-realistic novel views while preserving specific details of the input image.

Attribute Generative Adversarial Network +2

Real-Time Neural Character Rendering with Pose-Guided Multiplane Images

1 code implementation25 Apr 2022 Hao Ouyang, Bo Zhang, Pan Zhang, Hao Yang, Jiaolong Yang, Dong Chen, Qifeng Chen, Fang Wen

We propose pose-guided multiplane image (MPI) synthesis which can render an animatable character in real scenes with photorealistic quality.

Image-to-Image Translation Neural Rendering +1

Deep Video Prior for Video Consistency and Propagation

1 code implementation27 Jan 2022 Chenyang Lei, Yazhou Xing, Hao Ouyang, Qifeng Chen

A progressive propagation strategy with pseudo labels is also proposed to enhance DVP's performance on video propagation.

Optical Flow Estimation Semantic Segmentation +2

Image Inpainting with External-internal Learning and Monochromic Bottleneck

1 code implementation CVPR 2021 Tengfei Wang, Hao Ouyang, Qifeng Chen

Although recent inpainting approaches have demonstrated significant improvements with deep neural networks, they still suffer from artifacts such as blunt structures and abrupt colors when filling in the missing regions.

Image Inpainting

Neural Camera Simulators

1 code implementation CVPR 2021 Hao Ouyang, Zifan Shi, Chenyang Lei, Ka Lung Law, Qifeng Chen

To facilitate the learning of a simulator model, we collect a dataset of the 10, 000 raw images of 450 scenes with different exposure settings.

Data Augmentation

Human Pose Estimation with Spatial Contextual Information

no code implementations7 Jan 2019 Hong Zhang, Hao Ouyang, Shu Liu, Xiaojuan Qi, Xiaoyong Shen, Ruigang Yang, Jiaya Jia

With this principle, we present two conceptually simple and yet computational efficient modules, namely Cascade Prediction Fusion (CPF) and Pose Graph Neural Network (PGNN), to exploit underlying contextual information.

Pose Estimation

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