We present a new method for text-driven motion transfer - synthesizing a
video that complies with an input text prompt describing the target objects and
scene while maintaining an input video's motion and scene layout. Prior methods
are confined to transferring motion across two subjects within the same or
closely related object categories and are applicable for limited domains (e.g.,
humans). In this work, we consider a significantly more challenging setting in
which the target and source objects differ drastically in shape and
fine-grained motion characteristics (e.g., translating a jumping dog into a
dolphin). To this end, we leverage a pre-trained and fixed text-to-video
diffusion model, which provides us with generative and motion priors. The
pillar of our method is a new space-time feature loss derived directly from the
model. This loss guides the generation process to preserve the overall motion
of the input video while complying with the target object in terms of shape and
fine-grained motion traits.

我们提出了一种新的文本驱动运动转换方法，通过合成一个符合目标对象和场景描述的输入文本的视频，同时保持输入视频的运动和场景布局。

零样学习的基于文本驱动运动转换的时空扩散特征

Space-Time Diffusion Features for Zero-Shot Text-Driven Motion Transfer

Recent advancements in deep learning techniques have significantly improved
the quality of compressed videos. However, previous approaches have not fully
exploited the motion characteristics of compressed videos, such as the drastic
change in motion between video contents and the hierarchical coding structure
of the compressed video. This study proposes a novel framework that leverages
the low-delay configuration of video compression to enhance the existing
state-of-the-art method, BasicVSR++. We incorporate a context-adaptive video
fusion method to enhance the final quality of compressed videos. The proposed
approach has been evaluated in the NTIRE22 challenge, a benchmark for video
restoration and enhancement, and achieved improvements in both quantitative
metrics and visual quality compared to the previous method.

本文提出了一种新的框架，利用视频压缩的低延迟配置和上下文自适应视频融合方法，提高 BasicVSR ++ 方法的质量，已在 NTIRE22 挑战中得到验证，并在定量指标和视觉质量方面与之前的方法相比均有所提高。

利用视频编码知识进行深度视频增强

Leveraging Video Coding Knowledge for Deep Video Enhancement

Human figures have been animated using a variety of geometric models
including stick figures, polygonal models, and NURBS-based models with muscles,
flexible skin, or clothing. This paper reports on experimental results
indicating that a viewer's perception of motion characteristics is affected by
the geometric model used for rendering. Subjects were shown a series of paired
motion sequences and asked if the two motions in each pair were the same or
different. The motion sequences in each pair were rendered using the same
geometric model. For the three types of motion variation tested, sensitivity
scores indicate that subjects were better able to observe changes with the
polygonal model than they were with the stick figure model.

本文实验结果表明，渲染人物动作时使用的几何模型会影响观众对动作特征的感知，研究发现使用多边形模型较棒式骨骼模型更能提高观众对动作细节的观察能力。