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Guixun Luo

5 accepted papers

2026

StereoWorld: Geometry-Aware Monocular-to-Stereo Video Generation

CVPR 2026

The growing adoption of XR devices has fueled strong demand for high-quality stereo video, yet its production remains costly and artifact-prone.To address this challenge, we present **StereoWorld**, an **end-to-end framework** that repurposes a pretrained video generator for high-fidelity monocular-

Cited by 0SourceScholar
2026

VideoWorld 2: Learning Transferable Knowledge from Real-world Videos

CVPR 2026

Learning transferable knowledge from unlabeled video data and applying it in new environments is a fundamental capability of intelligent agents. This work presents VideoWorld 2, which extends VideoWorld and provides the first investigation of learning transferable knowledge for complex, long-horizon

Cited by 0SourceScholar
2025

Domain-Level Disentanglement Framework Based on Information Enhancement for Cross-Domain Cold-Start Recommendation

AAAI 2025technical

Recommender systems in various applications often encounter the challenge of cold-start, which refers to how to provide recommendations for completely new users. Cross-domain recommendation offers a solution to address this cold-start issue by leveraging user interaction information from other domai…

Cited by 0SourcePDFScholar
2025

Robust Graph Based Social Recommendation Through Contrastive Multi-View Learning

AAAI 2025technical

Social recommendation leverages the social connections between users to mitigate the issue of data sparsity and enhance recommendation quality. Although existing related works show their effectiveness, there remain two critical questions: i) The patterns of preference interactions among users are va…

Cited by 0SourcePDFScholar
2024

Graph Attention Network with High-Order Neighbor Information Propagation for Social Recommendation

IJCAI 2024poster

In recommender systems, graph neural networks (GNN) can integrate interactions between users and items with their attributes, which makes GNN-based methods more powerful. However, directly stacking multiple layers in a graph neural network can easily lead to over-smoothing, hence recommendation syst…

Cited by 2SourcePDFScholar