Search Results for author:
Found 23 papers, 13 papers with code
Perspective Transformer Nets: Learning Single-View 3D Object Reconstruction without 3D Supervision
We demonstrate the ability of the model in generating 3D volume from a single 2D image with three sets of experiments: (1) learning from single-class objects; (2) learning from multi-class objects and (3) testing on novel object classes.
NeRDi: Single-View NeRF Synthesis with Language-Guided Diffusion as General Image Priors
Formulating single-view reconstruction as an image-conditioned 3D generation problem, we optimize the NeRF representations by minimizing a diffusion loss on its arbitrary view renderings with a pretrained image diffusion model under the input-view constraint.
Generative Adversarial Text to Image Synthesis
Automatic synthesis of realistic images from text would be interesting and useful, but current AI systems are still far from this goal.
Block-NeRF: Scalable Large Scene Neural View Synthesis
We present Block-NeRF, a variant of Neural Radiance Fields that can represent large-scale environments.
Waymo Open Dataset: Panoramic Video Panoptic Segmentation
We therefore present the Waymo Open Dataset: Panoramic Video Panoptic Segmentation Dataset, a large-scale dataset that offers high-quality panoptic segmentation labels for autonomous driving.
Attribute2Image: Conditional Image Generation from Visual Attributes
This paper investigates a novel problem of generating images from visual attributes.
Learning Hierarchical Semantic Image Manipulation through Structured Representations
In this work, we present a novel hierarchical framework for semantic image manipulation.
SemanticAdv: Generating Adversarial Examples via Attribute-conditional Image Editing
In this paper, we aim to explore the impact of semantic manipulation on DNNs predictions by manipulating the semantic attributes of images and generate "unrestricted adversarial examples".
SemanticAdv: Generating Adversarial Examples via Attribute-conditioned Image Editing
Deep neural networks (DNNs) have achieved great successes in various vision applications due to their strong expressive power.
Learning Structured Output Representation using Deep Conditional Generative Models
The model is trained efficiently in the framework of stochastic gradient variational Bayes, and allows a fast prediction using stochastic feed-forward inference.
Ranked #1 on
Structured Prediction
on MNIST
PT2PC: Learning to Generate 3D Point Cloud Shapes from Part Tree Conditions
3D generative shape modeling is a fundamental research area in computer vision and interactive computer graphics, with many real-world applications.
MT-VAE: Learning Motion Transformations to Generate Multimodal Human Dynamics
Our model jointly learns a feature embedding for motion modes (that the motion sequence can be reconstructed from) and a feature transformation that represents the transition of one motion mode to the next motion mode.
Ranked #7 on
Human Pose Forecasting
on Human3.6M
(ADE metric)
Learning 6-DOF Grasping Interaction via Deep Geometry-aware 3D Representations
Our contributions are fourfold: (1) To best of our knowledge, we are presenting for the first time a method to learn a 6-DOF grasping net from RGBD input; (2) We build a grasping dataset from demonstrations in virtual reality with rich sensory and interaction annotations.
Deep Variational Canonical Correlation Analysis
We present deep variational canonical correlation analysis (VCCA), a deep multi-view learning model that extends the latent variable model interpretation of linear CCA to nonlinear observation models parameterized by deep neural networks.
Data-Efficient Learning for Sim-to-Real Robotic Grasping using Deep Point Cloud Prediction Networks
Training a deep network policy for robot manipulation is notoriously costly and time consuming as it depends on collecting a significant amount of real world data.
ShapeAdv: Generating Shape-Aware Adversarial 3D Point Clouds
Our shape-aware adversarial attacks are orthogonal to existing point cloud based attacks and shed light on the vulnerability of 3D deep neural networks.
Safety-Oriented Pedestrian Motion and Scene Occupancy Forecasting
On two large-scale real-world datasets, nuScenes and ATG4D, we showcase that our scene-occupancy predictions are more accurate and better calibrated than those from state-of-the-art motion forecasting methods, while also matching their performance in pedestrian motion forecasting metrics.
Exploring Adversarial Robustness of Multi-Sensor Perception Systems in Self Driving
Yet, there have been limited studies on the adversarial robustness of multi-modal models that fuse LiDAR features with image features.
S3: Neural Shape, Skeleton, and Skinning Fields for 3D Human Modeling
Constructing and animating humans is an important component for building virtual worlds in a wide variety of applications such as virtual reality or robotics testing in simulation.
GeoSim: Realistic Video Simulation via Geometry-Aware Composition for Self-Driving
Scalable sensor simulation is an important yet challenging open problem for safety-critical domains such as self-driving.
Motion Inspired Unsupervised Perception and Prediction in Autonomous Driving
Learning-based perception and prediction modules in modern autonomous driving systems typically rely on expensive human annotation and are designed to perceive only a handful of predefined object categories.
GINA-3D: Learning to Generate Implicit Neural Assets in the Wild
Modeling the 3D world from sensor data for simulation is a scalable way of developing testing and validation environments for robotic learning problems such as autonomous driving.
Unsupervised 3D Perception with 2D Vision-Language Distillation for Autonomous Driving
Closed-set 3D perception models trained on only a pre-defined set of object categories can be inadequate for safety critical applications such as autonomous driving where new object types can be encountered after deployment.
