Yihong Lin

Huilong Xie, Wenwei Song, Wenxiong Kang et al.

Recent advancements in both transformer-based methods and spiral neighbor sampling techniques have greatly enhanced hand mesh reconstruction. Transformers excel in capturing complex vertex relationships, and spiral neighbor sampling is vital for utilizing topological structures. This paper ingeniously integrates spiral sampling into the Transformer architecture, enhancing its ability to leverage mesh topology for superior performance in hand mesh reconstruction, resulting in substantial accuracy boosts. STMR employs a single image encoder for model efficiency. To augment its information extraction capability, we design the multi-scale pose feature extraction (MSPFE) module, which facilitates the extraction of rich pose features, ultimately enhancing the model's performance. Moreover, the proposed predefined pose-to-vertex lifting (PPVL) method improves vertex feature representation, further boosting reconstruction performance. Extensive experiments on the FreiHAND dataset demonstrate the state-of-the-art performance and unparalleled inference speed of STMR compared with similar backbone methods, showcasing its efficiency and effectiveness. The code is available at https://github.com/SmallXieGithub/STMR.

Yihong Lin, Zhaoxin Fan, Xianjia Wu et al.

Speech-driven talking head generation is a critical yet challenging task with applications in augmented reality and virtual human modeling. While recent approaches using autoregressive and diffusion-based models have achieved notable progress, they often suffer from modality inconsistencies, particularly misalignment between audio and mesh, leading to reduced motion diversity and lip-sync accuracy. To address this, we propose GLDiTalker, a novel speech-driven 3D facial animation model based on a Graph Latent Diffusion Transformer. GLDiTalker resolves modality misalignment by diffusing signals within a quantized spatiotemporal latent space. It employs a two-stage training pipeline: the Graph-Enhanced Quantized Space Learning Stage ensures lip-sync accuracy, while the Space-Time Powered Latent Diffusion Stage enhances motion diversity. Together, these stages enable GLDiTalker to generate realistic, temporally stable 3D facial animations. Extensive evaluations on standard benchmarks demonstrate that GLDiTalker outperforms existing methods, achieving superior results in both lip-sync accuracy and motion diversity.