3,343 research outputs found
Visual Object Networks: Image Generation with Disentangled 3D Representation
Recent progress in deep generative models has led to tremendous breakthroughs
in image generation. However, while existing models can synthesize
photorealistic images, they lack an understanding of our underlying 3D world.
We present a new generative model, Visual Object Networks (VON), synthesizing
natural images of objects with a disentangled 3D representation. Inspired by
classic graphics rendering pipelines, we unravel our image formation process
into three conditionally independent factors---shape, viewpoint, and
texture---and present an end-to-end adversarial learning framework that jointly
models 3D shapes and 2D images. Our model first learns to synthesize 3D shapes
that are indistinguishable from real shapes. It then renders the object's 2.5D
sketches (i.e., silhouette and depth map) from its shape under a sampled
viewpoint. Finally, it learns to add realistic texture to these 2.5D sketches
to generate natural images. The VON not only generates images that are more
realistic than state-of-the-art 2D image synthesis methods, but also enables
many 3D operations such as changing the viewpoint of a generated image, editing
of shape and texture, linear interpolation in texture and shape space, and
transferring appearance across different objects and viewpoints.Comment: NeurIPS 2018. Code: https://github.com/junyanz/VON Website:
http://von.csail.mit.edu
Learning a Disentangled Embedding for Monocular 3D Shape Retrieval and Pose Estimation
We propose a novel approach to jointly perform 3D shape retrieval and pose
estimation from monocular images.In order to make the method robust to
real-world image variations, e.g. complex textures and backgrounds, we learn an
embedding space from 3D data that only includes the relevant information,
namely the shape and pose. Our approach explicitly disentangles a shape vector
and a pose vector, which alleviates both pose bias for 3D shape retrieval and
categorical bias for pose estimation. We then train a CNN to map the images to
this embedding space, and then retrieve the closest 3D shape from the database
and estimate the 6D pose of the object. Our method achieves 10.3 median error
for pose estimation and 0.592 top-1-accuracy for category agnostic 3D object
retrieval on the Pascal3D+ dataset, outperforming the previous state-of-the-art
methods on both tasks
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
This paper describes InfoGAN, an information-theoretic extension to the
Generative Adversarial Network that is able to learn disentangled
representations in a completely unsupervised manner. InfoGAN is a generative
adversarial network that also maximizes the mutual information between a small
subset of the latent variables and the observation. We derive a lower bound to
the mutual information objective that can be optimized efficiently, and show
that our training procedure can be interpreted as a variation of the Wake-Sleep
algorithm. Specifically, InfoGAN successfully disentangles writing styles from
digit shapes on the MNIST dataset, pose from lighting of 3D rendered images,
and background digits from the central digit on the SVHN dataset. It also
discovers visual concepts that include hair styles, presence/absence of
eyeglasses, and emotions on the CelebA face dataset. Experiments show that
InfoGAN learns interpretable representations that are competitive with
representations learned by existing fully supervised methods
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