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 • 19 Dec 2023 • Jinghao Zhou, Tomas Jakab, Philip Torr, Christian Rupprecht
Recently, 3D generative models have made impressive progress, enabling the generation of almost arbitrary 3D assets from text or image inputs.
no code implementations • 4 Dec 2023 • Niki Amini-Naieni, Tomas Jakab, Andrea Vedaldi, Ronald Clark
To address this, we introduce the concept of a meta-calibrator that performs uncertainty calibration for NeRFs with a single forward pass without the need for holding out any images from the target scene.
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 • 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 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.
no code implementations • CVPR 2021 • Tomas Jakab, Richard Tucker, Ameesh Makadia, Jiajun Wu, Noah Snavely, Angjoo Kanazawa
We cast this as the problem of aligning a source 3D object to a target 3D object from the same object category.
no code implementations • CVPR 2020 • Tomas Jakab, Ankush Gupta, Hakan Bilen, Andrea Vedaldi
We propose KeypointGAN, a new method for recognizing the pose of objects from a single image that for learning uses only unlabelled videos and a weak empirical prior on the object poses.
2 code implementations • NeurIPS 2018 • Tomas Jakab, Ankush Gupta, Hakan Bilen, Andrea Vedaldi
We propose a method for learning landmark detectors for visual objects (such as the eyes and the nose in a face) without any manual supervision.