Search Results for author: Shangzhe Wu

Found 14 papers, 6 papers with code

Seeing a Rose in Five Thousand Ways

no code implementations9 Dec 2022 Yunzhi Zhang, Shangzhe Wu, Noah Snavely, Jiajun Wu

These instances all share the same intrinsics, but appear different due to a combination of variance within these intrinsics and differences in extrinsic factors, such as pose and illumination.

Image Generation Intrinsic Image Decomposition

CGOF++: Controllable 3D Face Synthesis with Conditional Generative Occupancy Fields

no code implementations23 Nov 2022 Keqiang Sun, Shangzhe Wu, Ning Zhang, Zhaoyang Huang, Quan Wang, Hongsheng Li

Capitalizing on the recent advances in image generation models, existing controllable face image synthesis methods are able to generate high-fidelity images with some levels of controllability, e. g., controlling the shapes, expressions, textures, and poses of the generated face images.

Face Generation

ONeRF: Unsupervised 3D Object Segmentation from Multiple Views

no code implementations22 Nov 2022 Shengnan Liang, Yichen Liu, Shangzhe Wu, Yu-Wing Tai, Chi-Keung Tang

We present ONeRF, a method that automatically segments and reconstructs object instances in 3D from multi-view RGB images without any additional manual annotations.

Semantic Segmentation

MagicPony: Learning Articulated 3D Animals in the Wild

no code implementations22 Nov 2022 Shangzhe Wu, Ruining Li, Tomas Jakab, Christian Rupprecht, Andrea Vedaldi

We consider the problem of learning a function that can estimate the 3D shape, articulation, viewpoint, texture, and lighting of an articulated animal like a horse, given a single test image.

Viewpoint Estimation

Controllable 3D Face Synthesis with Conditional Generative Occupancy Fields

no code implementations16 Jun 2022 Keqiang Sun, Shangzhe Wu, Zhaoyang Huang, Ning Zhang, Quan Wang, Hongsheng Li

Capitalizing on the recent advances in image generation models, existing controllable face image synthesis methods are able to generate high-fidelity images with some levels of controllability, e. g., controlling the shapes, expressions, textures, and poses of the generated face images.

Face Generation

De-rendering 3D Objects in the Wild

1 code implementation CVPR 2022 Felix Wimbauer, Shangzhe Wu, Christian Rupprecht

With increasing focus on augmented and virtual reality applications (XR) comes the demand for algorithms that can lift objects from images and videos into representations that are suitable for a wide variety of related 3D tasks.

DOVE: Learning Deformable 3D Objects by Watching Videos

no code implementations22 Jul 2021 Shangzhe Wu, Tomas Jakab, Christian Rupprecht, Andrea Vedaldi

In this paper, we present DOVE, a method that learns textured 3D models of deformable object categories from monocular videos available online, without keypoint, viewpoint or template shape supervision.

De-rendering the World's Revolutionary Artefacts

1 code implementation CVPR 2021 Shangzhe Wu, Ameesh Makadia, Jiajun Wu, Noah Snavely, Richard Tucker, Angjoo Kanazawa

Recent works have shown exciting results in unsupervised image de-rendering -- learning to decompose 3D shape, appearance, and lighting from single-image collections without explicit supervision.

NeRF--: Neural Radiance Fields Without Known Camera Parameters

4 code implementations14 Feb 2021 ZiRui Wang, Shangzhe Wu, Weidi Xie, Min Chen, Victor Adrian Prisacariu

Considering the problem of novel view synthesis (NVS) from only a set of 2D images, we simplify the training process of Neural Radiance Field (NeRF) on forward-facing scenes by removing the requirement of known or pre-computed camera parameters, including both intrinsics and 6DoF poses.

Novel View Synthesis

Self-Supervised Localisation between Range Sensors and Overhead Imagery

no code implementations3 Jun 2020 Tim Y. Tang, Daniele De Martini, Shangzhe Wu, Paul Newman

Publicly available satellite imagery can be an ubiquitous, cheap, and powerful tool for vehicle localisation when a prior sensor map is unavailable.

Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild

1 code implementation CVPR 2020 Shangzhe Wu, Christian Rupprecht, Andrea Vedaldi

We propose a method to learn 3D deformable object categories from raw single-view images, without external supervision.

Photo-Geometric Autoencoding to Learn 3D Objects from Unlabelled Images

no code implementations4 Jun 2019 Shangzhe Wu, Christian Rupprecht, Andrea Vedaldi

Specifically, given a single image of the object seen from an arbitrary viewpoint, our model predicts a symmetric canonical view, the corresponding 3D shape and a viewpoint transformation, and trains with the goal of reconstructing the input view, resembling an auto-encoder.

Deep High Dynamic Range Imaging with Large Foreground Motions

1 code implementation ECCV 2018 Shangzhe Wu, Jiarui Xu, Yu-Wing Tai, Chi-Keung Tang

In state-of-the-art deep HDR imaging, input images are first aligned using optical flows before merging, which are still error-prone due to occlusion and large motions.

Translation

Image Generation from Sketch Constraint Using Contextual GAN

1 code implementation ECCV 2018 Yongyi Lu, Shangzhe Wu, Yu-Wing Tai, Chi-Keung Tang

We train a generated adversarial network, i. e, contextual GAN to learn the joint distribution of sketch and the corresponding image by using joint images.

Image-to-Image Translation Translation

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