1,154 research outputs found
Dynamic Facial Expression Generation on Hilbert Hypersphere with Conditional Wasserstein Generative Adversarial Nets
In this work, we propose a novel approach for generating videos of the six
basic facial expressions given a neutral face image. We propose to exploit the
face geometry by modeling the facial landmarks motion as curves encoded as
points on a hypersphere. By proposing a conditional version of manifold-valued
Wasserstein generative adversarial network (GAN) for motion generation on the
hypersphere, we learn the distribution of facial expression dynamics of
different classes, from which we synthesize new facial expression motions. The
resulting motions can be transformed to sequences of landmarks and then to
images sequences by editing the texture information using another conditional
Generative Adversarial Network. To the best of our knowledge, this is the first
work that explores manifold-valued representations with GAN to address the
problem of dynamic facial expression generation. We evaluate our proposed
approach both quantitatively and qualitatively on two public datasets;
Oulu-CASIA and MUG Facial Expression. Our experimental results demonstrate the
effectiveness of our approach in generating realistic videos with continuous
motion, realistic appearance and identity preservation. We also show the
efficiency of our framework for dynamic facial expressions generation, dynamic
facial expression transfer and data augmentation for training improved emotion
recognition models
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
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