291 citations · 439 across the 7 of their papers we have counts for
8 papers
T3lescope: Arbitrary-Resolution High-Fidelity Generative Surface Reconstruction from Images
Atsuhiro Noguchi, Tianhan Xu, Yiming Liang +5
We reconstruct high-fidelity 3D scene meshes from posed multi-view images without per-scene optimization, across scales ranging from single objects to large outdoor scenes. Per-sce…
Multi-View Neural Surface Reconstruction with Structured Light
Chunyu Li, Taisuke Hashimoto, Eiichi Matsumoto +1
Three-dimensional (3D) object reconstruction based on differentiable rendering (DR) is an active research topic in computer vision. DR-based methods minimize the difference between…
Decomposing NeRF for Editing via Feature Field Distillation
Sosuke Kobayashi, Eiichi Matsumoto, Vincent Sitzmann
Emerging neural radiance fields (NeRF) are a promising scene representation for computer graphics, enabling high-quality 3D reconstruction and novel view synthesis from image obser…
Surface-Aligned Neural Radiance Fields for Controllable 3D Human Synthesis
Tianhan Xu, Yasuhiro Fujita, Eiichi Matsumoto
We propose a new method for reconstructing controllable implicit 3D human models from sparse multi-view RGB videos. Our method defines the neural scene representation on the mesh s…
Addressing Class Imbalance in Scene Graph Parsing by Learning to Contrast and Score
He Huang, Shunta Saito, Yuta Kikuchi +3
Scene graph parsing aims to detect objects in an image scene and recognize their relations. Recent approaches have achieved high average scores on some popular benchmarks, but fail…
Map-based Multi-Policy Reinforcement Learning: Enhancing Adaptability of Robots by Deep Reinforcement Learning
Ayaka Kume, Eiichi Matsumoto, Kuniyuki Takahashi +2
In order for robots to perform mission-critical tasks, it is essential that they are able to quickly adapt to changes in their environment as well as to injuries and or other bodil…
Learning Discrete Representations via Information Maximizing Self-Augmented Training
Weihua Hu, Takeru Miyato, Seiya Tokui +2
Learning discrete representations of data is a central machine learning task because of the compactness of the representations and ease of interpretation. The task includes cluster…
Temporal Generative Adversarial Nets with Singular Value Clipping
Masaki Saito, Eiichi Matsumoto, Shunta Saito
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 generati…