Search Results for author: Shih-Yang Su

Found 8 papers, 1 papers with code

UNeRF: Time and Memory Conscious U-Shaped Network for Training Neural Radiance Fields

no code implementations23 Jun 2022 Abiramy Kuganesan, Shih-Yang Su, James J. Little, Helge Rhodin

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.

Density Estimation Novel View Synthesis

DANBO: Disentangled Articulated Neural Body Representations via Graph Neural Networks

no code implementations3 May 2022 Shih-Yang Su, Timur Bagautdinov, Helge Rhodin

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.

Image Generation

3D Photography using Context-aware Layered Depth Inpainting

1 code implementation CVPR 2020 Meng-Li Shih, Shih-Yang Su, Johannes Kopf, Jia-Bin Huang

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.

Novel View Synthesis

Graph Generation with Variational Recurrent Neural Network

no code implementations2 Oct 2019 Shih-Yang Su, Hossein Hajimirsadeghi, Greg Mori

Generating graph structures is a challenging problem due to the diverse representations and complex dependencies among nodes.

Graph Generation Graph structure learning

Diversity-Driven Exploration Strategy for Deep Reinforcement Learning

no code implementations NeurIPS 2018 Zhang-Wei Hong, Tzu-Yun Shann, Shih-Yang Su, Yi-Hsiang Chang, Chun-Yi Lee

Efficient exploration remains a challenging research problem in reinforcement learning, especially when an environment contains large state spaces, deceptive local optima, or sparse rewards.

Efficient Exploration reinforcement-learning +1

Virtual-to-Real: Learning to Control in Visual Semantic Segmentation

no code implementations1 Feb 2018 Zhang-Wei Hong, Chen Yu-Ming, Shih-Yang Su, Tzu-Yun Shann, Yi-Hsiang Chang, Hsuan-Kung Yang, Brian Hsi-Lin Ho, Chih-Chieh Tu, Yueh-Chuan Chang, Tsu-Ching Hsiao, Hsin-Wei Hsiao, Sih-Pin Lai, Chun-Yi Lee

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.

Image Segmentation Semantic Segmentation

A Deep Policy Inference Q-Network for Multi-Agent Systems

no code implementations21 Dec 2017 Zhang-Wei Hong, Shih-Yang Su, Tzu-Yun Shann, Yi-Hsiang Chang, Chun-Yi Lee

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.

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