69 citations · 176 across the 11 of their papers we have counts for
11 papers
NPC: Neural Point Characters from Video
Shih-Yang Su, Timur Bagautdinov, Helge Rhodin
High-fidelity human 3D models can now be learned directly from videos, typically by combining a template-based surface model with neural representations. However, obtaining a templ…
Calibrationless Reconstruction of Uniformly-Undersampled Multi-Channel MR Data with Deep Learning Estimated ESPIRiT Maps
Junhao Zhang, Zheyuan Yi, Yujiao Zhao +8
Purpose: To develop a truly calibrationless reconstruction method that derives ESPIRiT maps from uniformly-undersampled multi-channel MR data by deep learning. Methods: ESPIRiT, on…
UNeRF: Time and Memory Conscious U-Shaped Network for Training Neural Radiance Fields
Abiramy Kuganesan, Shih-yang Su, James J. Little +1
Neural Radiance Fields (NeRFs) increase reconstruction detail for novel view synthesis and scene reconstruction, with applications ranging from large static scenes to dynamic human…
DANBO: Disentangled Articulated Neural Body Representations via Graph Neural Networks
Shih-Yang Su, Timur Bagautdinov, Helge Rhodin
Deep learning greatly improved the realism of animatable human models by learning geometry and appearance from collections of 3D scans, template meshes, and multi-view imagery. Hig…
A-NeRF: Articulated Neural Radiance Fields for Learning Human Shape, Appearance, and Pose
Shih-Yang Su, Frank Yu, Michael Zollhoefer +1
While deep learning reshaped the classical motion capture pipeline with feed-forward networks, generative models are required to recover fine alignment via iterative refinement. Un…
LaNet: Real-time Lane Identification by Learning Road SurfaceCharacteristics from Accelerometer Data
Madhumitha Harishankar, Jun Han, Sai Vineeth Kalluru Srinivas +7
The resolution of GPS measurements, especially in urban areas, is insufficient for identifying a vehicle's lane. In this work, we develop a deep LSTM neural network model LaNet tha…
3D Photography using Context-aware Layered Depth Inpainting
Meng-Li Shih, Shih-Yang Su, Johannes Kopf +1
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 str…
Graph Generation with Variational Recurrent Neural Network
Shih-Yang Su, Hossein Hajimirsadeghi, Greg Mori
Generating graph structures is a challenging problem due to the diverse representations and complex dependencies among nodes. In this paper, we introduce Graph Variational Recurren…
Diversity-Driven Exploration Strategy for Deep Reinforcement Learning
Zhang-Wei Hong, Tzu-Yun Shann, Shih-Yang Su +2
Efficient exploration remains a challenging research problem in reinforcement learning, especially when an environment contains large state spaces, deceptive local optima, or spars…
Virtual-to-Real: Learning to Control in Visual Semantic Segmentation
Zhang-Wei Hong, Chen Yu-Ming, Shih-Yang Su +10
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 si…
A Deep Policy Inference Q-Network for Multi-Agent Systems
Zhang-Wei Hong, Shih-Yang Su, Tzu-Yun Shann +2
We present DPIQN, a deep policy inference Q-network that targets multi-agent systems composed of controllable agents, collaborators, and opponents that interact with each other. We…