In recent years, the applications of digital humans have become increasingly widespread. One of the most challenging core technologies is the generation of highly realistic and automated 3D facial animation that combines facial movements and speech. The single-modal 3D facial animation driven by speech typically ignores the weak correlation between speech and upper facial movements as well as head posture. In contrast, the video-driven approach can perfectly solve the posture problem while obtaining natural expressions. However, mapping 2D facial information to 3D facial information may lead to information loss, which make lip synchronization generated by video-driven methods is not as good as the speech-driven methods trained on 4D facial data. Therefore, this paper proposes a dual-modal generation method that uses speech and video information to generate more natural and vivid 3D facial animation. Specifically, the lip movements related to speech are generated by speech-video information, while speech-uncorrelated postures and expressions are generated solely by video information. The speech-driven module is used to extract speech features, and its output lip animation is then used as the foundation for facial animation. The expression and pose module is used to extract temporal visual features for regressing expression and head posture parameters. We fuse speech and video features to obtain chin posture parameters related to lip movements, and use these parameters to fine-tune the lip animation generated form the speech-driven module. This paper introduces multiple consistency losses to enhance the network’s capability to generate expressions and postures. Experiments conducted on the LRS3, TCD-TIMIT and MEAD datasets show that the proposed method achieves better performance on evaluation metrics such as CER, WER, VER and VWER than the current state-of-the-art methods. In addition, a perceptual user study show that over 77% and 70% of cases believe that this paper’s method is better than the comparative algorithms EMOCA and SPECTRE in terms of realism. In terms of lip synchronization, it received over 79% and 66% of cases support, respectively. Both evaluation methods demonstrate the effectiveness of the proposed method.

Mineral image segmentation based on computer vision is vital to realize automatic mineral analysis. However, current image segmentation methods still cannot effectively solve the problem of sandstone grains that are adjoined and concealed by leaching processes, and the segmentation performance of small and irregular grains still needs to be improved. This investigation explores and designs a Mask R-CNN-based sandstone image segmentation model, including a hybrid attention mechanism, loss function construction, and receptive field enlargement. Simultaneously, we propose a high-quality sandstone dataset with abundant labels named SMISD to facilitate comprehensive training of the model. The experimental results show that the proposed segmentation model has excellent segmentation performance, effectively solving adhesion and overlap between adjacent grains without affecting the classification accuracy. The model has comparable performance to other models on the COCO dataset, and performs better on SMISD than others.

This study proposes an integrated computer vision system for automated petrological analysis of tight sandstone micro-structures. The system combines Zero-Shot Segmentation SAM (Segment Anything Model), Mask R-CNN (Region-Based Convolutional Neural Networks) instance segmentation, and an improved MetaFormer architecture with Cascaded Group Attention (CGA) attention mechanism, together with a parameter analysis module to form a hybrid deep learning system. This enables end-to-end mineral identification and multi-scale structural quantification of granulometric properties, grain contact relationships, and pore networks. The system is validated on proprietary tight sandstone datasets, SMISD (Sandstone Microscopic Image Segmentation Dataset)/SMIRD (Sandstone Microscopic Image Recognition Dataset). It achieves 92.1% mIoU segmentation accuracy and 90.7% mineral recognition accuracy while reducing processing time from more than 30 min to less than 2 min per sample. The system provides standardized reservoir characterization through automated generation of quantitative reports (Excel), analytical images (JPG), and structured data (JSON), demonstrating production-ready efficiency for tight sandstone evaluation.