29 citations · 43 across the 4 of their papers we have counts for
10 papers
S3: Neural Shape, Skeleton, and Skinning Fields for 3D Human Modeling
Ze Yang, Shenlong Wang, Sivabalan Manivasagam +5
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.…
Safety-Oriented Pedestrian Motion and Scene Occupancy Forecasting
Katie Luo, Sergio Casas, Renjie Liao +4
In this paper, we address the important problem in self-driving of forecasting multi-pedestrian motion and their shared scene occupancy map, critical for safe navigation. Our contr…
GeoSim: Realistic Video Simulation via Geometry-Aware Composition for Self-Driving
Yun Chen, Frieda Rong, Shivam Duggal +6
Scalable sensor simulation is an important yet challenging open problem for safety-critical domains such as self-driving. Current works in image simulation either fail to be photor…
ShapeAdv: Generating Shape-Aware Adversarial 3D Point Clouds
Kibok Lee, Zhuoyuan Chen, Xinchen Yan +2
We introduce ShapeAdv, a novel framework to study shape-aware adversarial perturbations that reflect the underlying shape variations (e.g., geometric deformations and structural di…
PT2PC: Learning to Generate 3D Point Cloud Shapes from Part Tree Conditions
Kaichun Mo, He Wang, Xinchen Yan +1
3D generative shape modeling is a fundamental research area in computer vision and interactive computer graphics, with many real-world applications. This paper investigates the nov…
Data-Efficient Learning for Sim-to-Real Robotic Grasping using Deep Point Cloud Prediction Networks
Xinchen Yan, Mohi Khansari, Jasmine Hsu +4
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. To work well in…