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Found 9 papers, 7 papers with code
A Tutorial on VAEs: From Bayes' Rule to Lossless Compression
The Variational Auto-Encoder (VAE) is a simple, efficient, and popular deep maximum likelihood model.
Adversarial shape perturbations on 3D point clouds
The importance of training robust neural network grows as 3D data is increasingly utilized in deep learning for vision tasks in robotics, drone control, and autonomous driving.
Extending Adversarial Attacks and Defenses to Deep 3D Point Cloud Classifiers
We present a preliminary evaluation of adversarial attacks on deep 3D point cloud classifiers, namely PointNet and PointNet++, by evaluating both white-box and black-box adversarial attacks that were proposed for 2D images and extending those attacks to reduce the perceptibility of the perturbations in 3D space.
Adversarial Defense by Stratified Convolutional Sparse Coding
We propose an adversarial defense method that achieves state-of-the-art performance among attack-agnostic adversarial defense methods while also maintaining robustness to input resolution, scale of adversarial perturbation, and scale of dataset size.
Deep Functional Dictionaries: Learning Consistent Semantic Structures on 3D Models from Functions
Even though our shapes have independent discretizations and no functional correspondences are provided, the network is able to generate latent bases, in a consistent order, that reflect the shared semantic structure among the shapes.
SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation
Experimental results on various 3D scenes show the effectiveness of our method on 3D instance segmentation, and we also evaluate the capability of SGPN to improve 3D object detection and semantic segmentation results.
Ranked #1 on
3D Semantic Instance Segmentation
on ScanNetV1
Realistic Dynamic Facial Textures From a Single Image Using GANs
By retargeting the PCA expression geometry from the source, as well as using the newly inferred texture, we can both animate the face and perform video face replacement on the source video using the target appearance.
Learning Dense Facial Correspondences in Unconstrained Images
To train such a network, we generate a massive dataset of synthetic faces with dense labels using renderings of a morphable face model with variations in pose, expressions, lighting, and occlusions.
Production-Level Facial Performance Capture Using Deep Convolutional Neural Networks
We present a real-time deep learning framework for video-based facial performance capture -- the dense 3D tracking of an actor's face given a monocular video.
