no code implementations • 4 Jan 2024 • Zizhang Li, Dor Litvak, Ruining Li, Yunzhi Zhang, Tomas Jakab, Christian Rupprecht, Shangzhe Wu, Andrea Vedaldi, Jiajun Wu
We show that prior category-specific attempts fail to generalize to rare species with limited training images.
no code implementations • 21 Dec 2023 • Keqiang Sun, Dor Litvak, Yunzhi Zhang, Hongsheng Li, Jiajun Wu, Shangzhe Wu
We introduce Ponymation, a new method for learning a generative model of articulated 3D animal motions from raw, unlabeled online videos.
no code implementations • 6 Dec 2023 • Sharon Lee, Yunzhi Zhang, Shangzhe Wu, Jiajun Wu
To encourage better disentanglement of different concept encoders, we anchor the concept embeddings to a set of text embeddings obtained from a pre-trained Visual Question Answering (VQA) model.
1 code implementation • NeurIPS 2023 • Zhengfei Kuang, Yunzhi Zhang, Hong-Xing Yu, Samir Agarwala, Shangzhe Wu, Jiajun Wu
We introduce Stanford-ORB, a new real-world 3D Object inverse Rendering Benchmark.
no code implementations • 20 Apr 2023 • Tomas Jakab, Ruining Li, Shangzhe Wu, Christian Rupprecht, Andrea Vedaldi
We propose a framework that uses an image generator, such as Stable Diffusion, to generate synthetic training data that are sufficiently clean and do not require further manual curation, enabling the learning of such a reconstruction network from scratch.
no code implementations • CVPR 2023 • 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.
no code implementations • 23 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.
no code implementations • CVPR 2023 • Shangzhe Wu, Ruining Li, Tomas Jakab, Christian Rupprecht, Andrea Vedaldi
We consider the problem of predicting the 3D shape, articulation, viewpoint, texture, and lighting of an articulated animal like a horse given a single test image as input.
no code implementations • 22 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.
no code implementations • 16 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.
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.
no code implementations • 22 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.
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.
5 code implementations • 14 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.
no code implementations • 3 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.
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.
no code implementations • 4 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.
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.
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.