1,342 research outputs found
Analyzing the Latent Space of GAN through Local Dimension Estimation
The impressive success of style-based GANs (StyleGANs) in high-fidelity image
synthesis has motivated research to understand the semantic properties of their
latent spaces. In this paper, we approach this problem through a geometric
analysis of latent spaces as a manifold. In particular, we propose a local
dimension estimation algorithm for arbitrary intermediate layers in a
pre-trained GAN model. The estimated local dimension is interpreted as the
number of possible semantic variations from this latent variable. Moreover,
this intrinsic dimension estimation enables unsupervised evaluation of
disentanglement for a latent space. Our proposed metric, called Distortion,
measures an inconsistency of intrinsic tangent space on the learned latent
space. Distortion is purely geometric and does not require any additional
attribute information. Nevertheless, Distortion shows a high correlation with
the global-basis-compatibility and supervised disentanglement score. Our work
is the first step towards selecting the most disentangled latent space among
various latent spaces in a GAN without attribute labels
Finding the global semantic representation in GAN through Frechet Mean
The ideally disentangled latent space in GAN involves the global
representation of latent space with semantic attribute coordinates. In other
words, considering that this disentangled latent space is a vector space, there
exists the global semantic basis where each basis component describes one
attribute of generated images. In this paper, we propose an unsupervised method
for finding this global semantic basis in the intermediate latent space in
GANs. This semantic basis represents sample-independent meaningful
perturbations that change the same semantic attribute of an image on the entire
latent space. The proposed global basis, called Fr\'echet basis, is derived by
introducing Fr\'echet mean to the local semantic perturbations in a latent
space. Fr\'echet basis is discovered in two stages. First, the global semantic
subspace is discovered by the Fr\'echet mean in the Grassmannian manifold of
the local semantic subspaces. Second, Fr\'echet basis is found by optimizing a
basis of the semantic subspace via the Fr\'echet mean in the Special Orthogonal
Group. Experimental results demonstrate that Fr\'echet basis provides better
semantic factorization and robustness compared to the previous methods.
Moreover, we suggest the basis refinement scheme for the previous methods. The
quantitative experiments show that the refined basis achieves better semantic
factorization while constrained on the same semantic subspace given by the
previous method.Comment: 25 pages, 21 figure
HandNeRF: Learning to Reconstruct Hand-Object Interaction Scene from a Single RGB Image
This paper presents a method to learn hand-object interaction prior for
reconstructing a 3D hand-object scene from a single RGB image. The inference as
well as training-data generation for 3D hand-object scene reconstruction is
challenging due to the depth ambiguity of a single image and occlusions by the
hand and object. We turn this challenge into an opportunity by utilizing the
hand shape to constrain the possible relative configuration of the hand and
object geometry. We design a generalizable implicit function, HandNeRF, that
explicitly encodes the correlation of the 3D hand shape features and 2D object
features to predict the hand and object scene geometry. With experiments on
real-world datasets, we show that HandNeRF is able to reconstruct hand-object
scenes of novel grasp configurations more accurately than comparable methods.
Moreover, we demonstrate that object reconstruction from HandNeRF ensures more
accurate execution of a downstream task, such as grasping for robotic
hand-over.Comment: 9 pages, 4 tables, 7 figure
Protocadherin-7 contributes to maintenance of bone homeostasis through regulation of osteoclast multinucleation
Kim, H., Takegahara, N., Walsh, M. C., Ueda, J., Fujihara, Y., Ikawa, M., & Choi, Y. (2020). Protocadherin-7 contributes to maintenance of bone homeostasis through regulation of osteoclast multinucleation. BMB Reports, 53(9), 472-477. doi:10.5483/BMBRep.2020.53.9.05
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