15 citations · 32 across the 5 of their papers we have counts for
6 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…
Unsupervised Learning of Efficient Geometry-Aware Neural Articulated Representations
Atsuhiro Noguchi, Xiao Sun, Stephen Lin +1
We propose an unsupervised method for 3D geometry-aware representation learning of articulated objects, in which no image-pose pairs or foreground masks are used for training. Thou…
Watch It Move: Unsupervised Discovery of 3D Joints for Re-Posing of Articulated Objects
Atsuhiro Noguchi, Umar Iqbal, Jonathan Tremblay +2
Rendering articulated objects while controlling their poses is critical to applications such as virtual reality or animation for movies. Manipulating the pose of an object, however…
Neural Articulated Radiance Field
Atsuhiro Noguchi, Xiao Sun, Stephen Lin +1
We present Neural Articulated Radiance Field (NARF), a novel deformable 3D representation for articulated objects learned from images. While recent advances in 3D implicit represen…
RGBD-GAN: Unsupervised 3D Representation Learning From Natural Image Datasets via RGBD Image Synthesis
Atsuhiro Noguchi, Tatsuya Harada
Understanding three-dimensional (3D) geometries from two-dimensional (2D) images without any labeled information is promising for understanding the real world without incurring ann…
Image Generation From Small Datasets via Batch Statistics Adaptation
Atsuhiro Noguchi, Tatsuya Harada
Thanks to the recent development of deep generative models, it is becoming possible to generate high-quality images with both fidelity and diversity. However, the training of such…