Search Results for author:
Found 35 papers, 14 papers with code
Non-parametric Memory for Spatio-Temporal Segmentation of Construction Zones for Self-Driving
In this paper, we introduce a non-parametric memory representation for spatio-temporal segmentation that captures the local space and time around an autonomous vehicle (AV).
Deep Structured Reactive Planning
An intelligent agent operating in the real-world must balance achieving its goal with maintaining the safety and comfort of not only itself, but also other participants within the surrounding scene.
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.
Diverse Complexity Measures for Dataset Curation in Self-driving
Our experiments on a wide range of tasks and models show that the proposed curation pipeline is able to select datasets that lead to better generalization and higher performance.
Hierarchical Verification for Adversarial Robustness
We show that, under certain conditions on the algorithm parameters, LayerCert provably reduces the number and size of the convex programs that one needs to solve compared to GeoCert.
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.
FAME: 3D Shape Generation via Functionality-Aware Model Evolution
Our tool supports constrained modeling, allowing users to restrict or steer the model evolution with functionality labels.
Graphics
Jointly Learnable Behavior and Trajectory Planning for Self-Driving Vehicles
The motion planners used in self-driving vehicles need to generate trajectories that are safe, comfortable, and obey the traffic rules.
Budgeted Training: Rethinking Deep Neural Network Training Under Resource Constraints
We also revisit existing approaches for fast convergence and show that budget-aware learning schedules readily outperform such approaches under (the practical but under-explored) budgeted training setting.
UPSNet: A Unified Panoptic Segmentation Network
More importantly, we introduce a parameter-free panoptic head which solves the panoptic segmentation via pixel-wise classification.
Ranked #3 on
Panoptic Segmentation
on Indian Driving Dataset
Photo-Sketching: Inferring Contour Drawings from Images
Edges, boundaries and contours are important subjects of study in both computer graphics and computer vision.
Real-Time Hair Rendering using Sequential Adversarial Networks
To handle the diversity of hairstyles and its appearance complexity, we disentangle hair structure, color, and illumination properties using a sequential GAN architecture and a semi-supervised training approach.
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 Blind Video Temporal Consistency
Our method takes the original unprocessed and per-frame processed videos as inputs to produce a temporally consistent video.
iMapper: Interaction-guided Joint Scene and Human Motion Mapping from Monocular Videos
A long-standing challenge in scene analysis is the recovery of scene arrangements under moderate to heavy occlusion, directly from monocular video.
Self-supervised Multi-view Person Association and Its Applications
Reliable markerless motion tracking of people participating in a complex group activity from multiple moving cameras is challenging due to frequent occlusions, strong viewpoint and appearance variations, and asynchronous video streams.
PlaneNet: Piece-wise Planar Reconstruction from a Single RGB Image
The proposed end-to-end DNN learns to directly infer a set of plane parameters and corresponding plane segmentation masks from a single RGB image.
Ranked #2 on
Plane Instance Segmentation
on NYU Depth v2
BodyNet: Volumetric Inference of 3D Human Body Shapes
Human shape estimation is an important task for video editing, animation and fashion industry.
Ranked #3 on
3D Human Pose Estimation
on Surreal
(using extra training data)
ST-GAN: Spatial Transformer Generative Adversarial Networks for Image Compositing
We address the problem of finding realistic geometric corrections to a foreground object such that it appears natural when composited into a background image.
Self-supervised Learning of Motion Capture
In this work, we propose a learning based motion capture model for single camera input.
Ranked #2 on
3D Human Reconstruction
on Surreal
SeeThrough: Finding Chairs in Heavily Occluded Indoor Scene Images
Discovering 3D arrangements of objects from single indoor images is important given its many applications including interior design, content creation, etc.
3D-PRNN: Generating Shape Primitives with Recurrent Neural Networks
The success of various applications including robotics, digital content creation, and visualization demand a structured and abstract representation of the 3D world from limited sensor data.
Material Editing Using a Physically Based Rendering Network
We propose an end-to-end network architecture that replicates the forward image formation process to accomplish this task.
Learning to Extract Semantic Structure From Documents Using Multimodal Fully Convolutional Neural Networks
We present an end-to-end, multimodal, fully convolutional network for extracting semantic structures from document images.
Learning Local Shape Descriptors from Part Correspondences With Multi-view Convolutional Networks
We present a new local descriptor for 3D shapes, directly applicable to a wide range of shape analysis problems such as point correspondences, semantic segmentation, affordance prediction, and shape-to-scan matching.
Learning to Extract Semantic Structure from Documents Using Multimodal Fully Convolutional Neural Network
We present an end-to-end, multimodal, fully convolutional network for extracting semantic structures from document images.
GRASS: Generative Recursive Autoencoders for Shape Structures
We introduce a novel neural network architecture for encoding and synthesis of 3D shapes, particularly their structures.
Neural Face Editing with Intrinsic Image Disentangling
Traditional face editing methods often require a number of sophisticated and task specific algorithms to be applied one after the other --- a process that is tedious, fragile, and computationally intensive.
Learning to Predict Indoor Illumination from a Single Image
We propose an automatic method to infer high dynamic range illumination from a single, limited field-of-view, low dynamic range photograph of an indoor scene.
Transformation-Grounded Image Generation Network for Novel 3D View Synthesis
Instead of taking a 'blank slate' approach, we first explicitly infer the parts of the geometry visible both in the input and novel views and then re-cast the remaining synthesis problem as image completion.
Physically-Based Rendering for Indoor Scene Understanding Using Convolutional Neural Networks
One of the bottlenecks in training for better representations is the amount of available per-pixel ground truth data that is required for core scene understanding tasks such as semantic segmentation, normal prediction, and object edge detection.
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.
