For reconstructing high-fidelity human 3D models from monocular videos, it is
crucial to maintain consistent large-scale body shapes along with finely
matched subtle wrinkles. This paper explores the observation that the per-frame
rendering results can be factorized into a pose-independent component and a
corresponding pose-dependent equivalent to facilitate frame consistency. Pose
adaptive textures can be further improved by restricting frequency bands of
these two components. In detail, pose-independent outputs are expected to be
low-frequency, while highfrequency information is linked to pose-dependent
factors. We achieve a coherent preservation of both coarse body contours across
the entire input video and finegrained texture features that are time variant
with a dual-branch network with distinct frequency components. The first branch
takes coordinates in canonical space as input, while the second branch
additionally considers features outputted by the first branch and pose
information of each frame. Our network integrates the information predicted by
both branches and utilizes volume rendering to generate photo-realistic 3D
human images. Through experiments, we demonstrate that our network surpasses
the neural radiance fields (NeRF) based state-of-the-art methods in preserving
high-frequency details and ensuring consistent body contours.

使用双分支网络和体积渲染生成照片级逼真的 3D 人体图像，通过限制频率分量和观察帧一致性，实现了保留高频细节和确保身体轮廓一致性的高保真度人体 3D 模型重建。

通过分解神经场表示可动化头像

Representing Animatable Avatar via Factorized Neural Fields

Previous face forgery detection methods mainly focus on appearance features,
which may be easily attacked by sophisticated manipulation. Considering the
majority of current face manipulation methods generate fake faces based on a
single frame, which do not take frame consistency and coordination into
consideration, artifacts on frame sequences are more effective for face forgery
detection. However, current sequence-based face forgery detection methods use
general video classification networks directly, which discard the special and
discriminative motion information for face manipulation detection. To this end,
we propose an effective sequence-based forgery detection framework based on an
existing video classification method. To make the motion features more
expressive for manipulation detection, we propose an alternative motion
consistency block instead of the original motion features module. To make the
learned features more generalizable, we propose an auxiliary anomaly detection
block. With these two specially designed improvements, we make a general video
classification network achieve promising results on three popular face forgery
datasets.

基于视频分类方法，提出了一种基于序列的伪造检测框架，以帧一致性、运动特征、视频分类为关键，取得了在三个常见人脸伪造数据集上有希望的结果。

学习表现力和可泛化的面部伪造检测运动特征

Learning Expressive And Generalizable Motion Features For Face Forgery  Detection

We present a diffusion-based video editing framework, namely DiffusionAtlas,
which can achieve both frame consistency and high fidelity in editing video
object appearance. Despite the success in image editing, diffusion models still
encounter significant hindrances when it comes to video editing due to the
challenge of maintaining spatiotemporal consistency in the object's appearance
across frames. On the other hand, atlas-based techniques allow propagating
edits on the layered representations consistently back to frames. However, they
often struggle to create editing effects that adhere correctly to the
user-provided textual or visual conditions due to the limitation of editing the
texture atlas on a fixed UV mapping field. Our method leverages a
visual-textual diffusion model to edit objects directly on the diffusion
atlases, ensuring coherent object identity across frames. We design a loss term
with atlas-based constraints and build a pretrained text-driven diffusion model
as pixel-wise guidance for refining shape distortions and correcting texture
deviations. Qualitative and quantitative experiments show that our method
outperforms state-of-the-art methods in achieving consistent high-fidelity
video-object editing.

我们提出了一种基于扩散的视频编辑框架，名为 DiffusionAtlas，可以在编辑视频对象外观时实现帧一致性和高保真度。

DiffusionAtlas: 高保真一致扩散视频编辑

DiffusionAtlas: High-Fidelity Consistent Diffusion Video Editing

Recently, transformer-based image segmentation methods have achieved notable
success against previous solutions. While for video domains, how to effectively
model temporal context with the attention of object instances across frames
remains an open problem. In this paper, we propose an online video instance
segmentation framework with a novel instance-aware temporal fusion method. We
first leverages the representation, i.e., a latent code in the global context
(instance code) and CNN feature maps to represent instance- and pixel-level
features. Based on this representation, we introduce a cropping-free temporal
fusion approach to model the temporal consistency between video frames.
Specifically, we encode global instance-specific information in the instance
code and build up inter-frame contextual fusion with hybrid attentions between
the instance codes and CNN feature maps. Inter-frame consistency between the
instance codes are further enforced with order constraints. By leveraging the
learned hybrid temporal consistency, we are able to directly retrieve and
maintain instance identities across frames, eliminating the complicated
frame-wise instance matching in prior methods. Extensive experiments have been
conducted on popular VIS datasets, i.e. Youtube-VIS-19/21. Our model achieves
the best performance among all online VIS methods. Notably, our model also
eclipses all offline methods when using the ResNet-50 backbone.

该论文提出了一种基于 transformer 的图像分割方法，利用一个新的实例感知的时间融合方法来处理视频序列中物体实例的时序关系，通过建立实例代码、CNN 特征图之间的混合注意力机制来实现跨帧的一致性建模，使得模型能够直接检测和跟踪视频序列中的物体实例。