Search Results for author:
Found 8 papers, 1 papers with code
UNeRF: Time and Memory Conscious U-Shaped Network for Training Neural Radiance Fields
Neural Radiance Fields (NeRFs) increase reconstruction detail for novel view synthesis and scene reconstruction, with applications ranging from large static scenes to dynamic human motion.
DANBO: Disentangled Articulated Neural Body Representations via Graph Neural Networks
While a few such approaches exist, those have limited generalization capabilities and are prone to learning spurious (chance) correlations between irrelevant body parts, resulting in implausible deformations and missing body parts on unseen poses.
A-NeRF: Articulated Neural Radiance Fields for Learning Human Shape, Appearance, and Pose
We propose a method to learn a generative neural body model from unlabelled monocular videos by extending Neural Radiance Fields (NeRFs).
3D Photography using Context-aware Layered Depth Inpainting
We propose a method for converting a single RGB-D input image into a 3D photo - a multi-layer representation for novel view synthesis that contains hallucinated color and depth structures in regions occluded in the original view.
Graph Generation with Variational Recurrent Neural Network
Generating graph structures is a challenging problem due to the diverse representations and complex dependencies among nodes.
Diversity-Driven Exploration Strategy for Deep Reinforcement Learning
Efficient exploration remains a challenging research problem in reinforcement learning, especially when an environment contains large state spaces, deceptive local optima, or sparse rewards.
Virtual-to-Real: Learning to Control in Visual Semantic Segmentation
Collecting training data from the physical world is usually time-consuming and even dangerous for fragile robots, and thus, recent advances in robot learning advocate the use of simulators as the training platform.
A Deep Policy Inference Q-Network for Multi-Agent Systems
DPIQN incorporates the learned policy features as a hidden vector into its own deep Q-network (DQN), such that it is able to predict better Q values for the controllable agents than the state-of-the-art deep reinforcement learning models.
