Speech-driven 3D facial animation has garnered lots of attention thanks to its broad range of applications. Despite recent advancements in achieving realistic lip motion, current methods fail to capture the nuanced emotional undertones conveyed through speech and produce monotonous facial motion. These limitations result in blunt and repetitive facial animations, reducing user engagement and hindering their applicability. To address these challenges, we introduce DEEPTalk, a novel approach that generates diverse and emotionally rich 3D facial expressions directly from speech inputs. To achieve this, we first train DEE (Dynamic Emotion Embedding), which employs probabilistic contrastive learning to forge a joint emotion embedding space for both speech and facial motion. This probabilistic framework captures the uncertainty in interpreting emotions from speech and facial motion, enabling the derivation of emotion vectors from its multifaceted space. Moreover, to generate dynamic facial motion, we design TH-VQVAE (Temporally Hierarchical VQ-VAE) as an expressive and robust motion prior overcoming limitations of VAEs and VQ-VAEs. Utilizing these strong priors, we develop DEEPTalk, a talking head generator that non-autoregressively predicts codebook indices to create dynamic facial motion, incorporating a novel emotion consistency loss. Extensive experiments on various datasets demonstrate the effectiveness of our approach in creating diverse, emotionally expressive talking faces that maintain accurate lip-sync. Our project page is available at https://whwjdqls.github.io/deeptalk.github.io/.

The paper describes a new approach to implement dynamically allocated multi-queue (DAMQ) switching elements using a technique called "self-compacting buffers". This technique is efficient in that the amount of hardware required to manage the buffers is relatively small; it offers high performance since it is an implementation of a DAMQ. The paper describes the self-compacting buffer architecture in detail, and compares it against a competing DAMQ switch design. It presents extensive simulation results comparing the performance of a self-compacting buffer switch against an ideal switch including several examples of k-ary n-cubes and delta networks. In addition, simulation results show how the performance of an entire network can be quickly and accurately approximated by simulating just a single switching element.>