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- Hybrid Transformer and CNN Attention Network for Stereo Image Super-resolution48
This work proposes a Hybrid Transformer and CNN Attention Network (HTCAN), which utilizes a transformer-based network for single-image enhancement and a CNN-basednetwork for stereo information fusion and employs a multi-patch training strategy and larger window sizes to activate more input pixels for super-resolution.
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- OPDN: Omnidirectional Position-aware Deformable Network for Omnidirectional Image Super-Resolution17
This work proposes a two-stage framework that achieves superior performance and wins the NTIRE 2023 challenge of 360° omnidirectional image super-resolution, and collects data from YouTube to improve the fitting ability of the transformer, and created pseudo low-resolution images using a degradation network.
- OmniDrag: Enabling Motion Control for Omnidirectional Image-to-Video Generation7
OmniDrag, the first approach enabling both scene- and object-level motion control for accurate, high-quality omnidirectional image-to-video generation, is proposed and an omnidirectional control module is introduced, which is jointly fine-tuned with temporal attention layers to effectively handle complex spherical motion.
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