5 accepted papers
In the domain of photorealistic talking head generation, the fidelity of audio-driven lip motion synthesis is essential for realistic virtual interactions. Existing methods face two key challenges: a lack of vivacity due to limited diversity in generated lip poses and noticeable anamorphose motions…
Video action recognition tasks face the trade-off challenge between computational cost and performance. Existing models often compromise their ability to capture extensive contextual information to reduce computational complexity. To address this issue, this paper proposes Large Kernel Selective Foc…
Existing image super-resolution (SR) methods often cause oversharpening, especially in defocus images. However, we found that defocus regions and focus regions have different levels of difficulty in recovery. This provides an opportunity for efficient enhancements. In this paper, we propose DefocusS…
In this paper, we propose a novel Enhanced Self-mined Text Guided Super-resolution Network (ESTGN) for single image super-resolution (SISR). Unlike preceding methods, ESTGN autonomously mines task-related text from images and uses it to guide SR for high-frequency detail restoration. The proposed me…
A key problem in blind image quality assessment (BIQA) is how to effectively model the properties of human visual system in a data-driven manner. In this paper, we propose a simple and efficient BIQA model based on a novel framework which consists of a fully convolutional neural network (FCNN) and a…