2.3k citations · 4.5k across the 63 of their papers we have counts for
12 papers · 1 filter
Learning Portrait Style Representations
Sadat Shaik, Bernadette Bucher, Nephele Agrafiotis +3
Style analysis of artwork in computer vision predominantly focuses on achieving results in target image generation through optimizing understanding of low level style characteristi…
Joint Estimation of Image Representations and their Lie Invariants
Christine Allen-Blanchette, Kostas Daniilidis
Images encode both the state of the world and its content. The former is useful for tasks such as planning and control, and the latter for classification. The automatic extraction…
Reinforcement Learning with Videos: Combining Offline Observations with Interaction
Karl Schmeckpeper, Oleh Rybkin, Kostas Daniilidis +2
Reinforcement learning is a powerful framework for robots to acquire skills from experience, but often requires a substantial amount of online data collection. As a result, it is d…
3D Bird Reconstruction: a Dataset, Model, and Shape Recovery from a Single View
Marc Badger, Yufu Wang, Adarsh Modh +5
Automated capture of animal pose is transforming how we study neuroscience and social behavior. Movements carry important social cues, but current methods are not able to robustly…
TLIO: Tight Learned Inertial Odometry
Wenxin Liu, David Caruso, Eddy Ilg +5
In this work we propose a tightly-coupled Extended Kalman Filter framework for IMU-only state estimation. Strap-down IMU measurements provide relative state estimates based on IMU…
Planning to Explore via Self-Supervised World Models
Ramanan Sekar, Oleh Rybkin, Kostas Daniilidis +3
Reinforcement learning allows solving complex tasks, however, the learning tends to be task-specific and the sample efficiency remains a challenge. We present Plan2Explore, a self-…
Coherent Reconstruction of Multiple Humans from a Single Image
Wen Jiang, Nikos Kolotouros, Georgios Pavlakos +2
In this work, we address the problem of multi-person 3D pose estimation from a single image. A typical regression approach in the top-down setting of this problem would first detec…
Simple and Effective VAE Training with Calibrated Decoders
Oleh Rybkin, Kostas Daniilidis, Sergey Levine
Variational autoencoders (VAEs) provide an effective and simple method for modeling complex distributions. However, training VAEs often requires considerable hyperparameter tuning…
Spin-Weighted Spherical CNNs
Carlos Esteves, Ameesh Makadia, Kostas Daniilidis
Learning equivariant representations is a promising way to reduce sample and model complexity and improve the generalization performance of deep neural networks. The spherical CNNs…
Spike-FlowNet: Event-based Optical Flow Estimation with Energy-Efficient Hybrid Neural Networks
Chankyu Lee, Adarsh Kumar Kosta, Alex Zihao Zhu +3
Event-based cameras display great potential for a variety of tasks such as high-speed motion detection and navigation in low-light environments where conventional frame-based camer…
An Adversarial Objective for Scalable Exploration
Bernadette Bucher, Karl Schmeckpeper, Nikolai Matni +1
Model-based curiosity combines active learning approaches to optimal sampling with the information gain based incentives for exploration presented in the curiosity literature. Exis…
Reactive Navigation in Partially Familiar Planar Environments Using Semantic Perceptual Feedback
Vasileios Vasilopoulos, Georgios Pavlakos, Karl Schmeckpeper +2
This paper solves the planar navigation problem by recourse to an online reactive scheme that exploits recent advances in SLAM and visual object recognition to recast prior geometr…