Talking head generation intends to produce vivid and realistic talking head videos from a single portrait and speech audio clip. Although significant progress has been made in diffusion-based talking head generation, almost all methods rely on autoregressive strategies, which suffer from limited context utilization beyond the current generation step, error accumulation, and slower generation speed. To address these challenges, we present DAWN (\textbf{D}ynamic frame \textbf{A}vatar \textbf{W}ith \textbf{N}on-autoregressive diffusion), a framework that enables all-at-once generation of dynamic-length video sequences. Specifically, it consists of two main components: (1) audio-driven holistic facial dynamics generation in the latent motion space, and (2) audio-driven head pose and blink generation. Extensive experiments demonstrate that our method generates authentic and vivid videos with precise lip motions, and natural pose/blink movements. Additionally, with a high generation speed, DAWN possesses strong extrapolation capabilities, ensuring the stable production of high-quality long videos. These results highlight the considerable promise and potential impact of DAWN in the field of talking head video generation. Furthermore, we hope that DAWN sparks further exploration of non-autoregressive approaches in diffusion models. Our code will be publicly available at \url{https://github.com/Hanbo-Cheng/DAWN-pytorch}.

Handwritten Mathematical Expression Recognition (HMER) forms a crucial task in the domain of document intelligence. It encompasses online and offline modalities, which utilize the trajectory sequence and static image as input, respectively. It is intuitive to utilize both online and offline modalities to build a more powerful recognition system. However, a formidable challenge arises as a result of the substantial heterogeneity between the online and offline modalities, which consequently leads to considerable obstacles in their alignment and fusion. In this work, we perceive the writing process as a video and introduce the Aggregated Optical Flow Map (AOFM) to represent the online modality, which is more compatible with the offline modality. Additionally, we propose the Optical Flow Aware Network (OFAN) in order to automatically extract, align, and fuse the features across online and offline modalities. Through experiment analysis, our method can be seamlessly applied to multiple existing offline HMER models, thereby yielding stable and substantial enhancements across CROHME 2014, 2016, and 2019 datasets. The code in this work is available at https: //github.com/Hanbo-Cheng/OFAN.git.