71 citations · 349 across the 23 of their papers we have counts for
23 papers
Local Deformation for Interactive Shape Editing
Honglin Chen, Changxi Zheng, Kevin Wampler
We introduce a novel regularization for localizing an elastic-energy-driven deformation to only those regions being manipulated by the user. Our local deformation features a natura…
Metalens Enhanced Ray Optics: An End-to-End Wave-Ray Co-Optimization Framework
Ziwei Zhu, Zhaocheng Liu, Changxi Zheng
We present a fully differentiable framework for seamlessly integrating wave optical components with geometrical lenses, offering an approach to enhance the performance of large-sca…
Sin3DM: Learning a Diffusion Model from a Single 3D Textured Shape
Rundi Wu, Ruoshi Liu, Carl Vondrick +1
Synthesizing novel 3D models that resemble the input example has long been pursued by graphics artists and machine learning researchers. In this paper, we present Sin3DM, a diffusi…
Learning to Generate 3D Shapes from a Single Example
Rundi Wu, Changxi Zheng
Existing generative models for 3D shapes are typically trained on a large 3D dataset, often of a specific object category. In this paper, we investigate the deep generative model t…
FishGym: A High-Performance Physics-based Simulation Framework for Underwater Robot Learning
Wenji Liu, Kai Bai, Xuming He +3
Bionic underwater robots have demonstrated their superiority in many applications. Yet, training their intelligence for a variety of tasks that mimic the behavior of underwater cre…
VarRCWA: An Adaptive High-Order Rigorous Coupled Wave Analysis Method
Ziwei Zhu, Changxi Zheng
Semi-analytical methods, such as rigorous coupled wave analysis, have been pivotal for numerical analysis of photonic structures. In comparison to other methods, they offer much fa…
DeepCAD: A Deep Generative Network for Computer-Aided Design Models
Rundi Wu, Chang Xiao, Changxi Zheng
Deep generative models of 3D shapes have received a great deal of research interest. Yet, almost all of them generate discrete shape representations, such as voxels, point clouds,…
Linear Semantics in Generative Adversarial Networks
Jianjin Xu, Changxi Zheng
Generative Adversarial Networks (GANs) are able to generate high-quality images, but it remains difficult to explicitly specify the semantics of synthesized images. In this work, w…
Listening to Sounds of Silence for Speech Denoising
Ruilin Xu, Rundi Wu, Yuko Ishiwaka +2
We introduce a deep learning model for speech denoising, a long-standing challenge in audio analysis arising in numerous applications. Our approach is based on a key observation ab…
Differentiable Scattering Matrix for Optimization of Photonic Structures
Ziwei Zhu, Changxi Zheng
The scattering matrix, which quantifies the optical reflection and transmission of a photonic structure, is pivotal for understanding the performance of the structure. In many phot…
One Man's Trash is Another Man's Treasure: Resisting Adversarial Examples by Adversarial Examples
Chang Xiao, Changxi Zheng
Modern image classification systems are often built on deep neural networks, which suffer from adversarial examples--images with deliberately crafted, imperceptible noise to mislea…
Enhancing Adversarial Defense by k-Winners-Take-All
Chang Xiao, Peilin Zhong, Changxi Zheng
We propose a simple change to existing neural network structures for better defending against gradient-based adversarial attacks. Instead of using popular activation functions (suc…
Mechanics-Aware Modeling of Cloth Appearance
Zahra Montazeri, Chang Xiao, Yun +3
Micro-appearance models have brought unprecedented fidelity and details to cloth rendering. Yet, these models neglect fabric mechanics: when a piece of cloth interacts with the env…
Dielectric Metasurfaces for Complete and Independent Control of Optical Amplitude and Phase
Adam C. Overvig, Sajan Shrestha, Stephanie C. Malek +4
Metasurfaces are optically thin metamaterials that promise complete control of the wavefront of light but are primarily used to control only the phase of light. Here, we present an…
Rethinking Generative Mode Coverage: A Pointwise Guaranteed Approach
Peilin Zhong, Yuchen Mo, Chang Xiao +2
Many generative models have to combat . The conventional wisdom to this end is by reducing through training a statistical distance (such as -divergence)…
Scene-Aware Audio for 360\textdegree{} Videos
Dingzeyu Li, Timothy R. Langlois, Changxi Zheng
Although 360\textdegree{} cameras ease the capture of panoramic footage, it remains challenging to add realistic 360\textdegree{} audio that blends into the captured scene and is s…
BourGAN: Generative Networks with Metric Embeddings
Chang Xiao, Peilin Zhong, Changxi Zheng
This paper addresses the mode collapse for generative adversarial networks (GANs). We view modes as a geometric structure of data distribution in a metric space. Under this geometr…
AirCode: Unobtrusive Physical Tags for Digital Fabrication
Dingzeyu Li, Avinash S. Nair, Shree K. Nayar +1
We present AirCode, a technique that allows the user to tag physically fabricated objects with given information. An AirCode tag consists of a group of carefully designed air pocke…
FontCode: Embedding Information in Text Documents using Glyph Perturbation
Chang Xiao, Cheng Zhang, Changxi Zheng
We introduce FontCode, an information embedding technique for text documents. Provided a text document with specific fonts, our method embeds user-specified information in the text…
Inverse Diffusion Curves using Shape Optimization
Shuang Zhao, Fredo Durand, Changxi Zheng
The inverse diffusion curve problem focuses on automatic creation of diffusion curve images that resemble user provided color fields. This problem is challenging since the 1D curve…
A fast implicit method for time-dependent Hamilton-Jacobi PDEs
Alexander Vladimirsky, Changxi Zheng
We present a new efficient computational approach for time-dependent first-order Hamilton-Jacobi-Bellman PDEs. Since our method is based on a time-implicit Eulerian discretization,…
Learning to Place New Objects in a Scene
Yun Jiang, Marcus Lim, Changxi Zheng +1
Placing is a necessary skill for a personal robot to have in order to perform tasks such as arranging objects in a disorganized room. The object placements should not only be stabl…
Learning to Place New Objects
Yun Jiang, Changxi Zheng, Marcus Lim +1
The ability to place objects in the environment is an important skill for a personal robot. An object should not only be placed stably, but should also be placed in its preferred l…