Search Results for author: Zhangsihao Yang

Found 9 papers, 1 papers with code

OmniMotionGPT: Animal Motion Generation with Limited Data

no code implementations CVPR 2024 Zhangsihao Yang, Mingyuan Zhou, Mengyi Shan, Bingbing Wen, Ziwei Xuan, Mitch Hill, Junjie Bai, Guo-Jun Qi, Yalin Wang

Our paper aims to generate diverse and realistic animal motion sequences from textual descriptions, without a large-scale animal text-motion dataset.

Motion Synthesis

TetCNN: Convolutional Neural Networks on Tetrahedral Meshes

no code implementations8 Feb 2023 Mohammad Farazi, Zhangsihao Yang, Wenhui Zhu, Peijie Qiu, Yalin Wang

Our results show the superiority of our LBO-based convolution layer and adapted pooling over the conventionally used unitary cortical thickness, graph Laplacian, and point cloud representation.

OTRE: Where Optimal Transport Guided Unpaired Image-to-Image Translation Meets Regularization by Enhancing

2 code implementations6 Feb 2023 Wenhui Zhu, Peijie Qiu, Oana M. Dumitrascu, Jacob M. Sobczak, Mohammad Farazi, Zhangsihao Yang, Keshav Nandakumar, Yalin Wang

Non-mydriatic retinal color fundus photography (CFP) is widely available due to the advantage of not requiring pupillary dilation, however, is prone to poor quality due to operators, systemic imperfections, or patient-related causes.

Denoising Diabetic Retinopathy Grading +5

Anisotropic Multi-Scale Graph Convolutional Network for Dense Shape Correspondence

no code implementations17 Oct 2022 Mohammad Farazi, Wenhui Zhu, Zhangsihao Yang, Yalin Wang

This paper studies 3D dense shape correspondence, a key shape analysis application in computer vision and graphics.

3D Dense Shape Correspondence

Continuous Geodesic Convolutions for Learning on 3D Shapes

no code implementations6 Feb 2020 Zhangsihao Yang, Or Litany, Tolga Birdal, Srinath Sridhar, Leonidas Guibas

In this work, we wish to challenge this practice and use a neural network to learn descriptors directly from the raw mesh.

3D Shape Synthesis for Conceptual Design and Optimization Using Variational Autoencoders

no code implementations16 Apr 2019 Wentai Zhang, Zhangsihao Yang, Haoliang Jiang, Suyash Nigam, Soji Yamakawa, Tomotake Furuhata, Kenji Shimada, Levent Burak Kara

We propose a data-driven 3D shape design method that can learn a generative model from a corpus of existing designs, and use this model to produce a wide range of new designs.

3D Shape Representation Decoder

3D Conceptual Design Using Deep Learning

no code implementations5 Aug 2018 Zhangsihao Yang, Haoliang Jiang, Zou Lan

Through this project, we expect the output can show a clear and smooth interpretation of model from different categories to develop a fast design support to generate novel shapes.

Data-driven Upsampling of Point Clouds

no code implementations8 Jul 2018 Wentai Zhang, Haoliang Jiang, Zhangsihao Yang, Soji Yamakawa, Kenji Shimada, Levent Burak Kara

High quality upsampling of sparse 3D point clouds is critically useful for a wide range of geometric operations such as reconstruction, rendering, meshing, and analysis.

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