191 citations · 425 across the 11 of their papers we have counts for
8 papers · 1 filter
Shape, Pose, and Appearance from a Single Image via Bootstrapped Radiance Field Inversion
Dario Pavllo, David Joseph Tan, Marie-Julie Rakotosaona +1
Neural Radiance Fields (NeRF) coupled with GANs represent a promising direction in the area of 3D reconstruction from a single view, owing to their ability to efficiently model arb…
Learning Generative Models of Textured 3D Meshes from Real-World Images
Dario Pavllo, Jonas Kohler, Thomas Hofmann +1
Recent advances in differentiable rendering have sparked an interest in learning generative models of textured 3D meshes from image collections. These models natively disentangle p…
Convolutional Generation of Textured 3D Meshes
Dario Pavllo, Graham Spinks, Thomas Hofmann +2
While recent generative models for 2D images achieve impressive visual results, they clearly lack the ability to perform 3D reasoning. This heavily restricts the degree of control…
Hierarchical Image Classification using Entailment Cone Embeddings
Ankit Dhall, Anastasia Makarova, Octavian Ganea +3
Image classification has been studied extensively, but there has been limited work in using unconventional, external guidance other than traditional image-label pairs for training.…
Controlling Style and Semantics in Weakly-Supervised Image Generation
Dario Pavllo, Aurelien Lucchi, Thomas Hofmann
We propose a weakly-supervised approach for conditional image generation of complex scenes where a user has fine control over objects appearing in the scene. We exploit sparse sema…
Modeling Human Motion with Quaternion-based Neural Networks
Dario Pavllo, Christoph Feichtenhofer, Michael Auli +1
Previous work on predicting or generating 3D human pose sequences regresses either joint rotations or joint positions. The former strategy is prone to error accumulation along the…
3D human pose estimation in video with temporal convolutions and semi-supervised training
Dario Pavllo, Christoph Feichtenhofer, David Grangier +1
In this work, we demonstrate that 3D poses in video can be effectively estimated with a fully convolutional model based on dilated temporal convolutions over 2D keypoints. We also…
QuaterNet: A Quaternion-based Recurrent Model for Human Motion
Dario Pavllo, David Grangier, Michael Auli
Deep learning for predicting or generating 3D human pose sequences is an active research area. Previous work regresses either joint rotations or joint positions. The former strateg…