Search Results for author: Yuanhao Zhai

Found 7 papers, 3 papers with code

Language-guided Human Motion Synthesis with Atomic Actions

1 code implementation18 Aug 2023 Yuanhao Zhai, Mingzhen Huang, Tianyu Luan, Lu Dong, Ifeoma Nwogu, Siwei Lyu, David Doermann, Junsong Yuan

In this paper, we propose ATOM (ATomic mOtion Modeling) to mitigate this problem, by decomposing actions into atomic actions, and employing a curriculum learning strategy to learn atomic action composition.

Motion Synthesis

High Fidelity 3D Hand Shape Reconstruction via Scalable Graph Frequency Decomposition

1 code implementation CVPR 2023 Tianyu Luan, Yuanhao Zhai, Jingjing Meng, Zhong Li, Zhang Chen, Yi Xu, Junsong Yuan

To capture high-frequency personalized details, we transform the 3D mesh into the frequency domain, and propose a novel frequency decomposition loss to supervise each frequency component.

DisCo: Disentangled Control for Realistic Human Dance Generation

1 code implementation30 Jun 2023 Tan Wang, Linjie Li, Kevin Lin, Yuanhao Zhai, Chung-Ching Lin, Zhengyuan Yang, Hanwang Zhang, Zicheng Liu, Lijuan Wang

In this paper, we depart from the traditional paradigm of human motion transfer and emphasize two additional critical attributes for the synthesis of human dance content in social media contexts: (i) Generalizability: the model should be able to generalize beyond generic human viewpoints as well as unseen human subjects, backgrounds, and poses; (ii) Compositionality: it should allow for composition of seen/unseen subjects, backgrounds, and poses from different sources seamlessly.

Attribute

SOAR: Scene-debiasing Open-set Action Recognition

no code implementations ICCV 2023 Yuanhao Zhai, Ziyi Liu, Zhenyu Wu, Yi Wu, Chunluan Zhou, David Doermann, Junsong Yuan, Gang Hua

Deep models have the risk of utilizing spurious clues to make predictions, e. g., recognizing actions via classifying the background scene.

Open Set Action Recognition Scene Classification

Two-Stream Consensus Network: Submission to HACS Challenge 2021 Weakly-Supervised Learning Track

no code implementations21 Jun 2021 Yuanhao Zhai, Le Wang, David Doermann, Junsong Yuan

The base model training encourages the model to predict reliable predictions based on single modality (i. e., RGB or optical flow), based on the fusion of which a pseudo ground truth is generated and in turn used as supervision to train the base models.

Optical Flow Estimation Weakly-supervised Learning +2

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