Search Results for author:
Found 19 papers, 7 papers with code
Learning the 3D Fauna of the Web
We show that prior category-specific attempts fail to generalize to rare species with limited training images.
Ponymation: Learning 3D Animal Motions from Unlabeled Online Videos
We introduce Ponymation, a new method for learning a generative model of articulated 3D animal motions from raw, unlabeled online videos.
Language-Informed Visual Concept Learning
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.
Stanford-ORB: A Real-World 3D Object Inverse Rendering Benchmark
We introduce Stanford-ORB, a new real-world 3D Object inverse Rendering Benchmark.
Farm3D: Learning Articulated 3D Animals by Distilling 2D Diffusion
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.
Seeing a Rose in Five Thousand Ways
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.
CGOF++: Controllable 3D Face Synthesis with Conditional Generative Occupancy Fields
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.
MagicPony: Learning Articulated 3D Animals in the Wild
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.
ONeRF: Unsupervised 3D Object Segmentation from Multiple Views
We present ONeRF, a method that automatically segments and reconstructs object instances in 3D from multi-view RGB images without any additional manual annotations.
Controllable 3D Face Synthesis with Conditional Generative Occupancy Fields
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.
De-rendering 3D Objects in the Wild
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
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
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
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.
Self-Supervised Localisation between Range Sensors and Overhead Imagery
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
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
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.
Image Generation from Sketch Constraint Using Contextual GAN
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.
Deep High Dynamic Range Imaging with Large Foreground Motions
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.
