Diffusion-based video generation models have demonstrated remarkable success
in obtaining high-fidelity videos through the iterative denoising process.
However, these models require multiple denoising steps during sampling,
resulting in high computational costs. In this work, we propose a novel
approach to obtain single-step video generation models by leveraging
adversarial training to fine-tune pre-trained video diffusion models. We show
that, through the adversarial training, the multi-steps video diffusion model,
i.e., Stable Video Diffusion (SVD), can be trained to perform single forward
pass to synthesize high-quality videos, capturing both temporal and spatial
dependencies in the video data. Extensive experiments demonstrate that our
method achieves competitive generation quality of synthesized videos with
significantly reduced computational overhead for the denoising process (i.e.,
around $23\times$ speedup compared with SVD and $6\times$ speedup compared with
existing works, with even better generation quality), paving the way for
real-time video synthesis and editing. More visualization results are made
publicly available at this https URL

通过对预训练的视频扩散模型进行对抗训练，我们提出了一种新颖的方法，通过单步合成高质量视频来获得单步视频生成模型，捕捉视频数据中的时间和空间依赖关系，从而大大减少计算成本，为实时视频合成和编辑铺平了道路。

SF-V：单向视频生成模型

SF-V: Single Forward Video Generation Model

Modeling and analyzing long sequences of text is an essential task for
Natural Language Processing. Success in capturing long text dynamics using
neural language models will facilitate many downstream tasks such as coherence
evaluation, text generation, machine translation and so on. This paper presents
a novel approach to model sequences through a stochastic process. We introduce
a likelihood-based training objective for the text encoder and design a more
thorough measurement (score) for long text evaluation compared to the previous
approach. The proposed training objective effectively preserves the sequence
coherence, while the new score comprehensively captures both temporal and
spatial dependencies. Theoretical properties of our new score show its
advantages in sequence evaluation. Experimental results show superior
performance in various sequence evaluation tasks, including global and local
discrimination within and between documents of different lengths. We also
demonstrate the encoder achieves competitive results on discriminating human
and AI written text.

通过一种新的概率模型和训练方法来建模长文本序列，提出了一种新的评估策略以维持序列的连贯性，并全面捕捉时空依赖关系，实验证明其在各种序列评估任务中表现出优异性能，并在人工智能和人类文本区分上取得竞争性结果。

基于随机过程的序列评估

On the Sequence Evaluation based on Stochastic Processes

A new method is proposed for human motion prediction by learning temporal and
spatial dependencies. Recently, multiscale graphs have been developed to model
the human body at higher abstraction levels, resulting in more stable motion
prediction. Current methods however predetermine scale levels and combine
spatially proximal joints to generate coarser scales based on human priors,
even though movement patterns in different motion sequences vary and do not
fully comply with a fixed graph of spatially connected joints. Another problem
with graph convolutional methods is mode collapse, in which predicted poses
converge around a mean pose with no discernible movements, particularly in
long-term predictions. To tackle these issues, we propose ResChunk, an
end-to-end network which explores dynamically correlated body components based
on the pairwise relationships between all joints in individual sequences.
ResChunk is trained to learn the residuals between target sequence chunks in an
autoregressive manner to enforce the temporal connectivities between
consecutive chunks. It is hence a sequence-to-sequence prediction network which
considers dynamic spatio-temporal features of sequences at multiple levels. Our
experiments on two challenging benchmark datasets, CMU Mocap and Human3.6M,
demonstrate that our proposed method is able to effectively model the sequence
information for motion prediction and outperform other techniques to set a new
state-of-the-art. Our code is available at
this https URL

提出了一种基于学习时空依赖关系的人体动作预测新方法，通过探索动态相关的身体组件之间的配对关系，以强制实施连续序列之间的时间连通性，从而有效地模拟运动序列信息，超越其他技术并树立新的技术标准。