Existing diffusion-based video editing models have made gorgeous advances for
editing attributes of a source video over time but struggle to manipulate the
motion information while preserving the original protagonist's appearance and
background. To address this, we propose MotionEditor, a diffusion model for
video motion editing. MotionEditor incorporates a novel content-aware motion
adapter into ControlNet to capture temporal motion correspondence. While
ControlNet enables direct generation based on skeleton poses, it encounters
challenges when modifying the source motion in the inverted noise due to
contradictory signals between the noise (source) and the condition (reference).
Our adapter complements ControlNet by involving source content to transfer
adapted control signals seamlessly. Further, we build up a two-branch
architecture (a reconstruction branch and an editing branch) with a
high-fidelity attention injection mechanism facilitating branch interaction.
This mechanism enables the editing branch to query the key and value from the
reconstruction branch in a decoupled manner, making the editing branch retain
the original background and protagonist appearance. We also propose a skeleton
alignment algorithm to address the discrepancies in pose size and position.
Experiments demonstrate the promising motion editing ability of MotionEditor,
both qualitatively and quantitatively.

MotionEditor 是一种扩散模型，用于视频动作编辑，通过引入内容感知的运动适配器和两分支架构实现编辑过程中保留原始背景和主角外貌。

MotionEditor: 通过内容感知扩散编辑视频动作

MotionEditor: Editing Video Motion via Content-Aware Diffusion

When some application scenarios need to use semantic segmentation technology,
like automatic driving, the primary concern comes to real-time performance
rather than extremely high segmentation accuracy. To achieve a good trade-off
between speed and accuracy, two-branch architecture has been proposed in recent
years. It treats spatial information and semantics information separately which
allows the model to be composed of two networks both not heavy. However, the
process of fusing features with two different scales becomes a performance
bottleneck for many nowaday two-branch models. In this research, we design a
new fusion mechanism for two-branch architecture which is guided by attention
computation. To be precise, we use the Dual-Guided Attention (DGA) module we
proposed to replace some multi-scale transformations with the calculation of
attention which means we only use several attention layers of near linear
complexity to achieve performance comparable to frequently-used multi-layer
fusion. To ensure that our module can be effective, we use Residual U-blocks
(RSU) to build one of the two branches in our networks which aims to obtain
better multi-scale features. Extensive experiments on Cityscapes and CamVid
dataset show the effectiveness of our method.

通过使用我们提出的双引导注意 (DGA) 模块对两个分支网络中的特征进行融合，我们在速度与准确性之间取得了良好的平衡，实现了与常用多层融合相当的性能。

双边网络与残差 U 块和双向引导注意力的实时语义分割

Bilateral Network with Residual U-blocks and Dual-Guided Attention for  Real-time Semantic Segmentation

High levels of noise usually exist in today's captured images due to the
relatively small sensors equipped in the smartphone cameras, where the noise
brings extra challenges to lossy image compression algorithms. Without the
capacity to tell the difference between image details and noise, general image
compression methods allocate additional bits to explicitly store the undesired
image noise during compression and restore the unpleasant noisy image during
decompression. Based on the observations, we optimize the image compression
algorithm to be noise-aware as joint denoising and compression to resolve the
bits misallocation problem. The key is to transform the original noisy images
to noise-free bits by eliminating the undesired noise during compression, where
the bits are later decompressed as clean images. Specifically, we propose a
novel two-branch, weight-sharing architecture with plug-in feature denoisers to
allow a simple and effective realization of the goal with little computational
cost. Experimental results show that our method gains a significant improvement
over the existing baseline methods on both the synthetic and real-world
datasets. Our source code is available at
this https URL

针对智能手机拍摄图像噪点问题，提出一种噪点感知的联合去噪与压缩图像算法，通过消除噪点并将其转换为无噪点位，以解决压缩图像时出现的误差位分配问题，实现了一个简单高效的两分支共享权重架构，并在合成和实际的数据集上获得了显著的性能提升。