Search Results for author:
Found 7 papers, 4 papers with code
Multi-View Neural Surface Reconstruction with Structured Light
Three-dimensional (3D) object reconstruction based on differentiable rendering (DR) is an active research topic in computer vision.
Decomposing NeRF for Editing via Feature Field Distillation
Emerging neural radiance fields (NeRF) are a promising scene representation for computer graphics, enabling high-quality 3D reconstruction and novel view synthesis from image observations.
Surface-Aligned Neural Radiance Fields for Controllable 3D Human Synthesis
Our method defines the neural scene representation on the mesh surface points and signed distances from the surface of a human body mesh.
Addressing Class Imbalance in Scene Graph Parsing by Learning to Contrast and Score
Motivated by the fact that detecting these rare relations can be critical in real-world applications, this paper introduces a novel integrated framework of classification and ranking to resolve the class imbalance problem in scene graph parsing.
Map-based Multi-Policy Reinforcement Learning: Enhancing Adaptability of Robots by Deep Reinforcement Learning
To solve this problem, we propose Map-based Multi-Policy Reinforcement Learning (MMPRL), which aims to search and store multiple policies that encode different behavioral features while maximizing the expected reward in advance of the environment change.
Learning Discrete Representations via Information Maximizing Self-Augmented Training
Learning discrete representations of data is a central machine learning task because of the compactness of the representations and ease of interpretation.
Ranked #3 on
Unsupervised Image Classification
on SVHN
(using extra training data)
Temporal Generative Adversarial Nets with Singular Value Clipping
In this paper, we propose a generative model, Temporal Generative Adversarial Nets (TGAN), which can learn a semantic representation of unlabeled videos, and is capable of generating videos.
