Xiang Li

Wenzhang Sun, Xiang Li, Donglin Di et al.

Recently, animating portrait images using audio input is a popular task. Creating lifelike talking head videos requires flexible and natural movements, including facial and head dynamics, camera motion, realistic light and shadow effects. Existing methods struggle to offer comprehensive, multifaceted control over these aspects. In this work, we introduce UniAvatar, a designed method that provides extensive control over a wide range of motion and illumination conditions. Specifically, we use the FLAME model to render all motion information onto a single image, maintaining the integrity of 3D motion details while enabling fine-grained, pixel-level control. Beyond motion, this approach also allows for comprehensive global illumination control. We design independent modules to manage both 3D motion and illumination, permitting separate and combined control. Extensive experiments demonstrate that our method outperforms others in both broad-range motion control and lighting control. Additionally, to enhance the diversity of motion and environmental contexts in current datasets, we collect and plan to publicly release two datasets, DH-FaceDrasMvVid-100 and DH-FaceReliVid-200, which capture significant head movements during speech and various lighting scenarios.

Xiang Li, Mingsi Liu, Lixin Duan

Purpose: Automatic and accurate segmentation of fundus vessel images has become an essential prerequisite for computer-aided diagnosis of ophthalmic diseases such as diabetes mellitus. The task of high-precision retinal vessel segmentation still faces difficulties due to the low contrast between the branch ends of retinal vessels and the background, the long and thin vessel span, and the variable morphology of the optic disc and optic cup in fundus vessel images. Methods: We propose a more advanced U-shaped architecture for a hybrid Transformer and CNN: TransUNext, which integrates an Efficient Self-attention Mechanism into the encoder and decoder of U-Net to capture both local features and global dependencies with minimal computational overhead. Meanwhile, the Global Multi-Scale Fusion (GMSF) module is further introduced to upgrade skip-connections, fuse high-level semantic and low-level detailed information, and eliminate high- and low-level semantic differences. Inspired by ConvNeXt, TransNeXt Block is designed to optimize the computational complexity of each base block in U-Net and avoid the information loss caused by the compressed dimension when the information is converted between the feature spaces of different dimensions. Results: We evaluated the proposed method on four public datasets DRIVE, STARE, CHASE-DB1, and HRF. In the experimental results, the AUC (area under the ROC curve) values were 0.9867, 0.9869, 0.9910, and 0.9887, which exceeded the other state-of-the-art.