As autonomous driving technology advances, the imperative of ensuring pedestrian traffic safety becomes increasingly prominent within the design framework of autonomous driving systems. Pedestrian trajectory prediction stands out as a pivotal technology aiming to address this challenge by striving to precisely forecast pedestrians’ future trajectories, thereby enabling autonomous driving systems to execute timely and accurate decisions. However, the prevailing state-of-the-art models often rely on intricate structures and a substantial number of parameters, posing challenges in meeting the imperative demand for lightweight models within autonomous driving systems. To address these challenges, we introduce Social Spatio-Temporal Graph Multi-Layer Perceptron (Social-STGMLP), a novel approach that utilizes solely fully connected layers and layer normalization. Social-STGMLP operates by abstracting pedestrian trajectories into a spatio-temporal graph, facilitating the modeling of both the spatial social interaction among pedestrians and the temporal motion tendency inherent to pedestrians themselves. Our evaluation of Social-STGMLP reveals its superiority over the reference method, as evidenced by experimental results indicating reductions of 5% in average displacement error (ADE) and 17% in final displacement error (FDE).

Accurate breast tumor segmentation in ultrasound images is a crucial step in medical diagnosis and locating the tumor region. However, segmentation faces numerous challenges due to the complexity of ultrasound images, similar intensity distributions, variable tumor morphology, and speckle noise. To address these challenges and achieve precise segmentation of breast tumors in complex ultrasound images, we propose a Synchronous Frequency-perception Fusion Network (Syn-Net). Initially, we design a synchronous dual-branch encoder to extract local and global feature information simultaneously from complex ultrasound images. Secondly, we introduce a novel Frequency- perception Cross-Feature Fusion (FrCFusion) Block, which utilizes Discrete Cosine Transform (DCT) to learn all-frequency features and effectively fuse local and global features while mitigating issues arising from similar intensity distributions. In addition, we develop a Full-Scale Deep Supervision method that not only corrects the influence of speckle noise on segmentation but also effectively guides decoder features towards the ground truth. We conduct extensive experiments on three publicly available ultrasound breast tumor datasets. Comparison with 14 state-of-the-art deep learning segmentation methods demonstrates that our approach exhibits superior sensitivity to different ultrasound images, variations in tumor size and shape, speckle noise, and similarity in intensity distribution between surrounding tissues and tumors. On the BUSI and Dataset B datasets, our method achieves better Dice scores compared to state-of-the-art methods, indicating superior performance in ultrasound breast tumor segmentation.

Audio-driven emotional talking face generation aims to generate talking face videos with rich facial expressions and temporal coherence. Current diffusion model-based approaches predominantly depend on either single-label emotion annotations or external video references, which often struggle to capture the complex relationships between modalities, resulting in less natural emotional expressions. To address these issues, we propose MF-ETalk, a multimodal feature-guided method for emotional talking face generation. Specifically, we design an emotion-aware multimodal feature disentanglement and fusion framework that leverages Action Units (AUs) to disentangle facial expressions and models the nonlinear relationships among AU features using a residual encoder. Furthermore, we introduce a hierarchical multimodal feature fusion module that enables dynamic interactions among audio, visual cues, AUs, and motion dynamics. This module is optimized through global motion modeling, lip synchronization, and expression subspace learning, enabling full-face dynamic generation. Finally, an emotion-consistency constraint module is employed to refine the generated results and ensure the naturalness of expressions. Extensive experiments on the MEAD and HDTF datasets demonstrate that MF-ETalk outperforms state-of-the-art methods in both expression naturalness and lip-sync accuracy. For example, it achieves an FID of 43.052 and E-FID of 2.403 on MEAD, along with strong synchronization performance (LSE-C of 6.781, LSE-D of 7.962), confirming the effectiveness of our approach in producing realistic and emotionally expressive talking face videos.

Face animation aims to render the source image according to the motion of the driving images while preserving the identity information of the source image. Despite significant advancements with the introduction of additional information, the literature lacks a thorough exploration of identity preservation. This paper proposes a Prior Structure-Assisted Identity-Preserving (PSAIP) network for face animation. Specifically, we introduce the additional priors of portrait segmentation images and face landmarks, which provide rich face geometric information while masking background interference to generate more realistic face images. Furthermore, we design an identity-enhancing generator network to adaptively fuses the identity feature information extracted from the source portrait and motion representations. Also, we utilize an identity-aware feature loss based on segmentation to avoid background distractions and monitor the identity preservation ability of the model. Extensive experiments on the VoxCeleb dataset and HDTF dataset show that our method is superior in identity preservation and realistic visual effects.