MoonshotAI
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Kimi-K3
Kimi K3 is an open-weight, 2.8T-parameter native multimodal agentic model with a 1M-token context window, designed for frontier coding, knowledge work, and reasoning tasks.
AI 仓库情报摘要
FR-AI / ANALYSIS为什么值得关注
它针对现有视频世界模型动作空间受限的问题，提出任意动作与帧级控制机制，并通过项目主页和论文公开方法；代码与权重计划在内部审核后发布。
适合谁使用
- 视频生成与世界模型方向的研究人员
- 研究动作条件动态的计算机视觉学者
- 构建交互式仿真或媒体工具的 AI 开发者
典型使用场景
- 在交互式视频生成中实现用户逐帧控制
- 从视频中学习动作表示，减少对人工动作标签的依赖
- 为任意动作输入构建基于视频的仿真环境
项目优势
- 提出面向交互式视频世界模型的任意动作、帧级控制
- 引入从视频及其阴影中学习的统一动态表示
- 提供 arXiv 论文和项目主页，便于了解方法
- 计划在内部审核后发布代码和模型权重
使用前须知
- 代码和模型权重尚未公开
- README 未提供基准测试数据或系统级对比
- 现阶段使用需依赖论文内容，并等待官方发布
README 快速开始
ShadowDancer
Teaching Video World Models Any Action from a Video and Its Shadow
ShadowDancer: Teaching Video World Models Any Action by Learning Unified Dynamics Representations from a Video and Its Shadow
Jin Cao, Zian Meng, Kaipeng Zhang† († corresponding author)
ShadowDancer gives interactive video world models an any-action, frame-level control.
The code and model weights are being cleaned up and are undergoing internal review before release.🚧
Citation
@misc{cao2026shadow,
title={ShadowDancer: Teaching Video World Models Any Action by Learning Unified Dynamics Representations from a Video and Its Shadow},
author={Jin Cao and Zian Meng and Kaipeng Zhang},
year={2026},
eprint={2607.28362},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2607.28362},
}
项目描述
Code release for https://shadowdancer-1.github.io/
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