Cong Liang

Zhenjie Liu, Jianzhang Lu, Renjie Lu et al.

Recent advancements in video diffusion models have significantly enhanced audio-driven portrait animation. However, current methods still suffer from flickering, identity drift, and poor audio-visual synchronization. These issues primarily stem from entangled appearance-motion representations and unstable inference strategies. In this paper, we introduce \textbf{ConsistTalk}, a novel intensity-controllable and temporally consistent talking head generation framework with diffusion noise search inference. First, we propose \textbf{an optical flow-guided temporal module (OFT)} that decouples motion features from static appearance by leveraging facial optical flow, thereby reducing visual flicker and improving temporal consistency. Second, we present an \textbf{Audio-to-Intensity (A2I) model} obtained through multimodal teacher-student knowledge distillation. By transforming audio and facial velocity features into a frame-wise intensity sequence, the A2I model enables joint modeling of audio and visual motion, resulting in more natural dynamics. This further enables fine-grained, frame-wise control of motion dynamics while maintaining tight audio-visual synchronization. Third, we introduce a \textbf{diffusion noise initialization strategy (IC-Init)}. By enforcing explicit constraints on background coherence and motion continuity during inference-time noise search, we achieve better identity preservation and refine motion dynamics compared to the current autoregressive strategy. Extensive experiments demonstrate that ConsistTalk significantly outperforms prior methods in reducing flicker, preserving identity, and delivering temporally stable, high-fidelity talking head videos.

Yunze Wei, Tianshuo Hu, Cong Liang et al.

The past few years have witnessed the flourishing of large-scale deep neural network models with ever-growing parameter numbers. Training such large-scale models typically requires massive memory and computing resources, necessitating distributed training. As GPU performance has rapidly evolved in recent years, computation time has shrunk, making communication a larger portion of the overall training time. Consequently, optimizing communication for distributed training has become crucial. In this article, we briefly introduce the general architecture of distributed deep neural network training and analyze relationships among Parallelization Strategy, Collective Communication Library, and Network from the perspective of communication optimization, which forms a three-layer paradigm. We then review current representative research advances within this three-layer paradigm. We find that layers in the current three-layer paradigm are relatively independent and there is a rich design space for cross-layer collaborative optimization in distributed training scenarios. Therefore, we advocate "Vertical" and "Horizontal" co-designs which extend the three-layer paradigm to a five-layer paradigm. We also advocate "Intra-Inter" and "Host-Net" co-designs to further utilize the potential of heterogeneous resources. We hope this article can shed some light on future research on communication optimization for distributed training.