Shijun Shi — accepted AI-conference papers

2026

One-to-All Animation: Alignment-Free Character Animation and Image Pose Transfer

Shijun Shi, Jing Xu, Zhihang Li, Chunli Peng, Xiaoda Yang, Lijing Lu +2

CVPR 2026

Recent advances in diffusion models have greatly improved pose-driven character animation. However, existing methods are limited to spatially aligned reference-pose pairs with matched skeletal structures. Handling reference-pose misalignment remains unsolved. To address this, we present One-to-All A

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2025

Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution

Shijun Shi, Jing Xu, Lijing Lu, Zhihang Li, Kai Hu

CVPR 2025poster

Existing diffusion-based video super-resolution (VSR) methods are susceptible to introducing complex degradations and noticeable artifacts into high-resolution videos due to their inherent randomness. In this paper, we propose a noise-robust real-world VSR framework by incorporating self-supervised…

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