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Ziyang Song
OSN: Infinite Representations of Dynamic 3D Scenes from Monocular Videos Ziyang Song , Jinxi Li, Bo Yang
International Conference on Machine Learning (ICML ) , 2024
arXiv /
Video Code
The first framework to represent dynamic 3D scenes in infinitely many ways from a monocular RGB video.
Unsupervised 3D Object Segmentation of Point Clouds by Geometry Consistency Ziyang Song , Bo Yang
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI ) , 2024
IEEE Xplore /
Video Code
The journal version of our OGC (NeurIPS 2022). More experiments and analysis are included.
NVFi: Neural Velocity Fields for 3D Physics Learning from Dynamic Videos Ziyang Song , Bo Yang
Advances in Neural Information Processing Systems (NeurIPS ) , 2023
arXiv /
Code
A novel representation of dynamic 3D scenes by disentangling physical velocities from geometry and appearance, enabling: 1) future frame extrapolation, 2) unsupervised semantic scene decomposition, and 3) velocity transfer.
ActFormer: A GAN-based Transformer towards General Action-Conditioned 3D Human Motion Generation Ziyang Song* , Dongliang Wang, Jing Su, Zhicheng Fang, Chenjing Ding, Weihao Gan, Yichao Yan, Xin Jin, Xiaokang Yang, Wenjun Zeng, Wei Wu
International Conference on Computer Vision (ICCV ) , 2023
arXiv /
Project Page Code
(* denotes equal contribution)
A GAN-based Transformer for general action-conditioned 3D human motion generation, including single-person actions and multi-person interactive actions.
OGC: Unsupervised 3D Object Segmentation from Rigid Dynamics of Point Clouds Ziyang Song , Bo Yang
Advances in Neural Information Processing Systems (NeurIPS ) , 2022
arXiv /
Video Code
We propose the first unsupervised 3D object segmentation method, learning from dynamic motion patterns in point cloud sequences.
