Detail features of magnetic resonance images play a cru-cial role in accurate
medical diagnosis and treatment, as they capture subtle changes that pose
challenges for doc-tors when performing precise judgments. However, the widely
utilized naive diffusion model has limitations, as it fails to accurately
capture more intricate details. To en-hance the quality of MRI reconstruction,
we propose a comprehensive detail-preserving reconstruction method using
multiple diffusion models to extract structure and detail features in k-space
domain instead of image do-main. Moreover, virtual binary modal masks are
utilized to refine the range of values in k-space data through highly adaptive
center windows, which allows the model to focus its attention more efficiently.
Last but not least, an inverted pyramid structure is employed, where the
top-down image information gradually decreases, ena-bling a cascade
representation. The framework effective-ly represents multi-scale sampled data,
taking into ac-count the sparsity of the inverted pyramid architecture, and
utilizes cascade training data distribution to repre-sent multi-scale data.
Through a step-by-step refinement approach, the method refines the
approximation of de-tails. Finally, the proposed method was evaluated by
con-ducting experiments on clinical and public datasets. The results
demonstrate that the proposed method outper-forms other methods.

通过使用多种扩散模型提取 k 空间域中的结构和细节特征，利用虚拟二进制模态掩码和倒金字塔结构来增强 MRI 重建质量，方法通过逐步细化逼近细节并采用级联训练数据分布来表示多尺度数据，从而有效地捕捉和呈现高分辨率的 MRI 图像。

DP-MDM：通过多个扩散模型实现细节保留的磁共振重建

DP-MDM: Detail-Preserving MR Reconstruction via Multiple Diffusion  Models

Character Animation aims to generating character videos from still images
through driving signals. Currently, diffusion models have become the mainstream
in visual generation research, owing to their robust generative capabilities.
However, challenges persist in the realm of image-to-video, especially in
character animation, where temporally maintaining consistency with detailed
information from character remains a formidable problem. In this paper, we
leverage the power of diffusion models and propose a novel framework tailored
for character animation. To preserve consistency of intricate appearance
features from reference image, we design ReferenceNet to merge detail features
via spatial attention. To ensure controllability and continuity, we introduce
an efficient pose guider to direct character's movements and employ an
effective temporal modeling approach to ensure smooth inter-frame transitions
between video frames. By expanding the training data, our approach can animate
arbitrary characters, yielding superior results in character animation compared
to other image-to-video methods. Furthermore, we evaluate our method on
benchmarks for fashion video and human dance synthesis, achieving
state-of-the-art results.

通过扩展训练数据，我们提出了一种针对角色动画的新型框架，使用扩散模型来保持细节特征的一致性，通过空间注意力合并细节特征，引入高效的姿势指导器来控制角色运动，并采用有效的时间建模方法来实现视频帧之间的流畅过渡，相比其他图像到视频方法，在角色动画领域取得了卓越的结果。此外，我们在时尚视频和人类舞蹈合成基准上评估了我们的方法，取得了最先进的结果。

实现动画的任何人：一致且可控的角色动画图像到视频的合成

Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for  Character Animation

This work presents AnyDoor, a diffusion-based image generator with the power
to teleport target objects to new scenes at user-specified locations in a
harmonious way. Instead of tuning parameters for each object, our model is
trained only once and effortlessly generalizes to diverse object-scene
combinations at the inference stage. Such a challenging zero-shot setting
requires an adequate characterization of a certain object. To this end, we
complement the commonly used identity feature with detail features, which are
carefully designed to maintain texture details yet allow versatile local
variations (e.g., lighting, orientation, posture, etc.), supporting the object
in favorably blending with different surroundings. We further propose to borrow
knowledge from video datasets, where we can observe various forms (i.e., along
the time axis) of a single object, leading to stronger model generalizability
and robustness. Extensive experiments demonstrate the superiority of our
approach over existing alternatives as well as its great potential in
real-world applications, such as virtual try-on and object moving. Project page
is this https URL

AnyDoor 是一种基于扩散的图像生成器，它能够以和谐的方式将目标对象传送到用户指定的位置。通过使用细节特征来补充身份特征，该模型能够在推理阶段对不同的对象 - 场景组合进行普适化。通过借鉴视频数据集的知识，该方法在现实世界的虚拟试穿和物体移动等应用中展现出优越性和巨大潜力。