5,782 research outputs found
GAGAN: Geometry-Aware Generative Adversarial Networks
Deep generative models learned through adversarial training have become
increasingly popular for their ability to generate naturalistic image textures.
However, aside from their texture, the visual appearance of objects is
significantly influenced by their shape geometry; information which is not
taken into account by existing generative models. This paper introduces the
Geometry-Aware Generative Adversarial Networks (GAGAN) for incorporating
geometric information into the image generation process. Specifically, in GAGAN
the generator samples latent variables from the probability space of a
statistical shape model. By mapping the output of the generator to a canonical
coordinate frame through a differentiable geometric transformation, we enforce
the geometry of the objects and add an implicit connection from the prior to
the generated object. Experimental results on face generation indicate that the
GAGAN can generate realistic images of faces with arbitrary facial attributes
such as facial expression, pose, and morphology, that are of better quality
than current GAN-based methods. Our method can be used to augment any existing
GAN architecture and improve the quality of the images generated
Reducing "Structure From Motion": a General Framework for Dynamic Vision - Part 1: Modeling
The literature on recursive estimation of structure and motion from monocular image sequences comprises a large number of different models and estimation techniques. We propose a framework that allows us to derive and compare all models by following the idea of dynamical system reduction.
The "natural" dynamic model, derived by the rigidity constraint and the perspective projection, is first reduced by explicitly decoupling structure (depth) from motion. Then implicit decoupling techniques are explored, which consist of imposing that some function of the unknown parameters is held constant. By appropriately choosing such a function, not only can we account for all models seen so far in the literature, but we can also derive novel ones
Stable Frank-Kasper phases of self-assembled, soft matter spheres
Single molecular species can self-assemble into Frank Kasper (FK) phases,
finite approximants of dodecagonal quasicrystals, defying intuitive notions
that thermodynamic ground states are maximally symmetric. FK phases are
speculated to emerge as the minimal-distortional packings of space-filling
spherical domains, but a precise quantitation of this distortion and how it
affects assembly thermodynamics remains ambiguous. We use two complementary
approaches to demonstrate that the principles driving FK lattice formation in
diblock copolymers emerge directly from the strong-stretching theory of
spherical domains, in which minimal inter-block area competes with minimal
stretching of space-filling chains. The relative stability of FK lattices is
studied first using a diblock foam model with unconstrained particle volumes
and shapes, which correctly predicts not only the equilibrium {\sigma} lattice,
but also the unequal volumes of the equilibrium domains. We then provide a
molecular interpretation for these results via self-consistent field theory,
illuminating how molecular stiffness regulates the coupling between
intra-domain chain configurations and the asymmetry of local packing. These
findings shed new light on the role of volume exchange on the formation of
distinct FK phases in copolymers, and suggest a paradigm for formation of FK
phases in soft matter systems in which unequal domain volumes are selected by
the thermodynamic competition between distinct measures of shape asymmetry.Comment: 40 pages, 22 figure
- …