Junyu Xie

I am currently a fourth-year DPhil student at Visual Geometry Group (VGG), University of Oxford, advised by Prof. Andrew Zisserman and Prof. Weidi Xie.

Prior to that, I completed my undergraduate studies at University of Cambridge and received MSc and BA degrees in Natural Sciences (Physics), during which I did summer interns on machine learning and physics at Caltech, Fudan University, and University of Cambridge.

Email  /  Google Scholar  /  Github

My research focuses on long-form video understanding, object-centric learning, and motion segmentation. I am also interested in representation learning, image and video generation, and multimodal language model.

@article{xie2024autoad0,
  title={AutoAD-Zero: A Training-Free Framework for Zero-Shot Audio Description},
  author={Junyu Xie and Tengda Han and Max Bain and Arsha Nagrani and G\"ul Varol and Weidi Xie and Andrew Zisserman},
  journal={arXiv preprint arXiv:2407.15850},
  year={2024}
}
            

In this paper, we propose AutoAD-Zero, which is a training-free framework aiming at zero-shot Audio Description (AD) generation for movies and TV series. The overall framework feature two stages (dense description + AD summary), with the character information injected by visual-textual prompting.

@article{xie2024flowsam,
  title={Moving Object Segmentation: All You Need Is SAM (and Flow)},
  author={Junyu Xie and Charig Yang and Weidi Xie and Andrew Zisserman},
  journal={arXiv preprint arXiv:2404.12389},
  year={2024}
}
            

This paper focuses on motion segmentation by incorporating optical flow into the Segment Anything model (SAM), applying flow information as direct inputs (FlowISAM) or prompts (FlowPSAM).

@InProceedings{xie2024appearrefine,
  title={Appearance-Based Refinement for Object-Centric Motion Segmentation},
  author={Junyu Xie and Weidi Xie and Andrew Zisserman},
  booktitle={ECCV},
  year={2024}
}
            

This paper aims at improving flow-only motion segmentation (e.g. OCLR predictions) by leveraging appearance information across video frames. A selection-correction pipeline is developed, along with a test-time model adaptation scheme that further alleviates the Sim2Real disparity.

@InProceedings{chen2024shap,
  title={SHAP-EDITOR: Instruction-guided Latent 3D Editing in Seconds},
  author={Chen, Minghao and Xie, Junyu and Laina, Iro and Vedaldi, Andrea},
  booktitle=CVPR,
  year={2024}
}
                

This paper present a method, named SHAP-EDITOR, aiming at fast 3D editing (within one second). To acheve this, we propose to learn a universal editing function that can be applied to different objects in a feed-forward manner.

@InProceedings{xie2022segmenting,
  title     = {Segmenting Moving Objects via an Object-Centric Layered Representation}, 
  author    = {Junyu Xie and Weidi Xie and Andrew Zisserman},
  booktitle = {NeurIPS},
  year      = {2022}
}
                

In this paper, we propose the OCLR model for discovering, tracking and segmenting multiple moving objects in a video without relying on human annotations. This object-centric segmentation model utilises depth-ordered layered representations and is trained following a Sim2Real procedure.

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