2,392 research outputs found
GRASS: Generative Recursive Autoencoders for Shape Structures
We introduce a novel neural network architecture for encoding and synthesis
of 3D shapes, particularly their structures. Our key insight is that 3D shapes
are effectively characterized by their hierarchical organization of parts,
which reflects fundamental intra-shape relationships such as adjacency and
symmetry. We develop a recursive neural net (RvNN) based autoencoder to map a
flat, unlabeled, arbitrary part layout to a compact code. The code effectively
captures hierarchical structures of man-made 3D objects of varying structural
complexities despite being fixed-dimensional: an associated decoder maps a code
back to a full hierarchy. The learned bidirectional mapping is further tuned
using an adversarial setup to yield a generative model of plausible structures,
from which novel structures can be sampled. Finally, our structure synthesis
framework is augmented by a second trained module that produces fine-grained
part geometry, conditioned on global and local structural context, leading to a
full generative pipeline for 3D shapes. We demonstrate that without
supervision, our network learns meaningful structural hierarchies adhering to
perceptual grouping principles, produces compact codes which enable
applications such as shape classification and partial matching, and supports
shape synthesis and interpolation with significant variations in topology and
geometry.Comment: Corresponding author: Kai Xu ([email protected]
EVA3D: Compositional 3D Human Generation from 2D Image Collections
Inverse graphics aims to recover 3D models from 2D observations. Utilizing
differentiable rendering, recent 3D-aware generative models have shown
impressive results of rigid object generation using 2D images. However, it
remains challenging to generate articulated objects, like human bodies, due to
their complexity and diversity in poses and appearances. In this work, we
propose, EVA3D, an unconditional 3D human generative model learned from 2D
image collections only. EVA3D can sample 3D humans with detailed geometry and
render high-quality images (up to 512x256) without bells and whistles (e.g.
super resolution). At the core of EVA3D is a compositional human NeRF
representation, which divides the human body into local parts. Each part is
represented by an individual volume. This compositional representation enables
1) inherent human priors, 2) adaptive allocation of network parameters, 3)
efficient training and rendering. Moreover, to accommodate for the
characteristics of sparse 2D human image collections (e.g. imbalanced pose
distribution), we propose a pose-guided sampling strategy for better GAN
learning. Extensive experiments validate that EVA3D achieves state-of-the-art
3D human generation performance regarding both geometry and texture quality.
Notably, EVA3D demonstrates great potential and scalability to
"inverse-graphics" diverse human bodies with a clean framework.Comment: Project Page at https://hongfz16.github.io/projects/EVA3D.htm
Self-supervised learning methods and applications in medical imaging analysis: A survey
The scarcity of high-quality annotated medical imaging datasets is a major
problem that collides with machine learning applications in the field of
medical imaging analysis and impedes its advancement. Self-supervised learning
is a recent training paradigm that enables learning robust representations
without the need for human annotation which can be considered an effective
solution for the scarcity of annotated medical data. This article reviews the
state-of-the-art research directions in self-supervised learning approaches for
image data with a concentration on their applications in the field of medical
imaging analysis. The article covers a set of the most recent self-supervised
learning methods from the computer vision field as they are applicable to the
medical imaging analysis and categorize them as predictive, generative, and
contrastive approaches. Moreover, the article covers 40 of the most recent
research papers in the field of self-supervised learning in medical imaging
analysis aiming at shedding the light on the recent innovation in the field.
Finally, the article concludes with possible future research directions in the
field
- …