no code implementations • 4 Apr 2024 • Zixuan Huang, Justin Johnson, Shoubhik Debnath, James M. Rehg, Chao-yuan Wu
We present PointInfinity, an efficient family of point cloud diffusion models.
1 code implementation • 4 Mar 2024 • Dmitry Tochilkin, David Pankratz, Zexiang Liu, Zixuan Huang, Adam Letts, Yangguang Li, Ding Liang, Christian Laforte, Varun Jampani, Yan-Pei Cao
This technical report introduces TripoSR, a 3D reconstruction model leveraging transformer architecture for fast feed-forward 3D generation, producing 3D mesh from a single image in under 0. 5 seconds.
3D Generation 3D Object Reconstruction From A Single Image +2
no code implementations • 1 Jan 2024 • Ke Yang, Jiateng Liu, John Wu, Chaoqi Yang, Yi R. Fung, Sha Li, Zixuan Huang, Xu Cao, Xingyao Wang, Yiquan Wang, Heng Ji, ChengXiang Zhai
The prominent large language models (LLMs) of today differ from past language models not only in size, but also in the fact that they are trained on a combination of natural language and formal language (code).
no code implementations • 21 Dec 2023 • Zixuan Huang, Stefan Stojanov, Anh Thai, Varun Jampani, James M. Rehg
In contrast, the traditional approach to this problem is regression-based, where deterministic models are trained to directly regress the object shape.
1 code implementation • NeurIPS 2023 • Anh Thai, Ahmad Humayun, Stefan Stojanov, Zixuan Huang, Bikram Boote, James M. Rehg
This paper introduces Low-shot Object Learning with Mutual Exclusivity Bias (LSME), the first computational framing of mutual exclusivity bias, a phenomenon commonly observed in infants during word learning.
1 code implementation • 20 Jul 2023 • Ashish Singh, Prateek Agarwal, Zixuan Huang, Arpita Singh, Tong Yu, Sungchul Kim, Victor Bursztyn, Nikos Vlassis, Ryan A. Rossi
Captions are crucial for understanding scientific visualizations and documents.
no code implementations • CVPR 2023 • Zixuan Huang, Varun Jampani, Anh Thai, Yuanzhen Li, Stefan Stojanov, James M. Rehg
We present ShapeClipper, a novel method that reconstructs 3D object shapes from real-world single-view RGB images.
no code implementations • 19 Feb 2023 • Zixuan Huang, Xingyu Lin, David Held
In this work, we propose a self-supervised method to finetune a mesh reconstruction model in the real world.
1 code implementation • 28 Nov 2022 • Stefan Stojanov, Anh Thai, Zixuan Huang, James M. Rehg
A hallmark of the deep learning era for computer vision is the successful use of large-scale labeled datasets to train feature representations for tasks ranging from object recognition and semantic segmentation to optical flow estimation and novel view synthesis of 3D scenes.
no code implementations • 6 Jun 2022 • Zixuan Huang, Xingyu Lin, David Held
We evaluate our system both on cloth flattening as well as on cloth canonicalization, in which the objective is to manipulate the cloth into a canonical pose.
no code implementations • 24 May 2022 • Zixuan Huang, Yunfeng Wang, Zhiwen Chen, Xin Gao, Ruili Feng, Xiaobo Li
Skeleton extraction is a task focused on providing a simple representation of an object by extracting the skeleton from the given binary or RGB image.
no code implementations • 21 Apr 2022 • Zixuan Huang, Stefan Stojanov, Anh Thai, Varun Jampani, James M. Rehg
We present a novel 3D shape reconstruction method which learns to predict an implicit 3D shape representation from a single RGB image.
no code implementations • 14 Oct 2021 • Chenxi Wang, Yunfeng Wang, Zixuan Huang, Zhiwen Chen
Global human motion forecasting is important in many fields, which is the combination of global human trajectory prediction and local human pose prediction.
1 code implementation • 21 May 2021 • Xingyu Lin, YuFei Wang, Zixuan Huang, David Held
Robotic manipulation of cloth remains challenging for robotics due to the complex dynamics of the cloth, lack of a low-dimensional state representation, and self-occlusions.
3 code implementations • 18 Jan 2021 • Anh Thai, Stefan Stojanov, Zixuan Huang, Isaac Rehg, James M. Rehg
Continual learning has been extensively studied for classification tasks with methods developed to primarily avoid catastrophic forgetting, a phenomenon where earlier learned concepts are forgotten at the expense of more recent samples.
1 code implementation • CVPR 2020 • Zixuan Huang, Yin Li
Our results compare favorably to state-of-the-art methods on classification tasks, and our method outperforms previous approaches on the localization of object parts.
1 code implementation • 29 Oct 2019 • Zixuan Huang, Jinghuai Zhang, Jing Liao
Recent neural style transfer frameworks have obtained astonishing visual quality and flexibility in Single-style Transfer (SST), but little attention has been paid to Multi-style Transfer (MST) which refers to simultaneously transferring multiple styles to the same image.
no code implementations • 27 Aug 2018 • Zixuan Huang, Junming Fan, Shenggan Cheng, Shuai Yi, Xiaogang Wang, Hongsheng Li
Dense depth cues are important and have wide applications in various computer vision tasks.
Ranked #10 on Depth Completion on KITTI Depth Completion