Generating rich and controllable motion is a pivotal challenge in video
synthesis. We propose Boximator, a new approach for fine-grained motion
control. Boximator introduces two constraint types: hard box and soft box.
Users select objects in the conditional frame using hard boxes and then use
either type of boxes to roughly or rigorously define the object's position,
shape, or motion path in future frames. Boximator functions as a plug-in for
existing video diffusion models. Its training process preserves the base
model's knowledge by freezing the original weights and training only the
control module. To address training challenges, we introduce a novel
self-tracking technique that greatly simplifies the learning of box-object
correlations. Empirically, Boximator achieves state-of-the-art video quality
(FVD) scores, improving on two base models, and further enhanced after
incorporating box constraints. Its robust motion controllability is validated
by drastic increases in the bounding box alignment metric. Human evaluation
also shows that users favor Boximator generation results over the base model.

Boximator 是一种新的视频合成方法，通过引入硬盒和软盒两种约束类型，在条件帧中选择目标物体并在未来帧中粗略或严格定义其位置、形状或运动路径，以实现丰富且可控的运动；在训练过程中，通过冻结原始权重并仅训练控制模块，保持基础模型的知识；通过引入一种新颖的自我追踪技术，大大简化了目标物体与盒子的相关性学习；实验证明，Boximator 在视频质量和运动可控性上均取得了最先进的结果，并经过引入盒子约束进一步提升，用户评估结果也表明用户喜欢 Boximator 的生成结果。

Boximator: 生成富有丰富和可控的动作用于视频合成

Boximator: Generating Rich and Controllable Motions for Video Synthesis

Federated learning holds great promise in learning from fragmented sensitive
data and has revolutionized how machine learning models are trained. This
article provides a systematic overview and detailed taxonomy of federated
learning. We investigate the existing security challenges in federated learning
and provide a comprehensive overview of established defense techniques for data
poisoning, inference attacks, and model poisoning attacks. The work also
presents an overview of current training challenges for federated learning,
focusing on handling non-i.i.d. data, high dimensionality issues, and
heterogeneous architecture, and discusses several solutions for the associated
challenges. Finally, we discuss the remaining challenges in managing federated
learning training and suggest focused research directions to address the open
questions. Potential candidate areas for federated learning, including IoT
ecosystem, healthcare applications, are discussed with a particular focus on
banking and financial domains.

本文提供了一种系统的概述和分类，涵盖了联邦学习的现有安全挑战以及针对数据污染，推断攻击和模型毒化攻击的防御技术。此外，还探讨了处理非独立同分布数据、高维度问题和异构架构的联邦学习的当前训练挑战，并提出了解决相关挑战的几种解决方案。最后，我们讨论了联邦学习训练中剩余的挑战，并提出了针对开放问题的研究方向建议。