# EgoCS-400K：面向世界模型的第一人称游戏数据集

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-06-16 08:00
- AIHOT 分数：51
- AIHOT 链接：https://aihot.virxact.com/items/cmqi29vj504vnslf07x0ga9ft
- 原文链接：https://arxiv.org/abs/2606.18180

## AI 摘要

EgoCS-400K是基于CS/CS2职业比赛demo构建的大规模第一人称数据集，保留了人类游戏轨迹并支持解析、回放、渲染和时间对齐。包含超过40万段第一人称视频、1万小时游戏内容，来自1000多场比赛和4万回合，覆盖13张地图、每回合10个玩家视角。数据集提取了玩家状态、视角方向、移动、键盘/按键输入、视角变化、武器使用、游戏事件和回合上下文，并渲染出干净的第一人称视频。支持动作条件未来预测、状态与事件场景展开、回放字幕生成及智能体第一人称动作理解等任务，连接了被动网络视频、可控游戏仿真和昂贵的真实世界具身数据。

## 正文

The shift from video generation to interactive world modeling places new demands on data: beyond captioned videos, world models require temporally aligned video-action-language trajectories grounded in the actions, camera motion, states, and events that drive future scene changes. However, such data is difficult to obtain at scale. Web video datasets offer broad visual coverage but lack executable actions and reliable states; robotic datasets provide action and state supervision but are costly and limited in scene diversity; and existing simulators often lack large-scale human-driven interaction trajectories. In this paper, we introduce EgoCS-400K, a large-scale replay-grounded egocentric Counter-Strike dataset for world models, built from public professional CS and CS2 match demos that preserve human gameplay trajectories and enable parsing, replaying, rendering, and temporal alignment. We extract player states, view directions, movements, keyboard/button inputs, view-angle changes, weapon usage, game events, and round-level context, and render clean first-person videos from the same trajectories. EgoCS-400K contains over 400,000 first-person videos and 10,000 hours of gameplay from more than 1,000 matches and 40,000 rounds, covering 13 maps and 10 player viewpoints per round. It supports a range of interactive visual modeling tasks, including action-conditioned future prediction, state- and event-aware scene rollout, replay-grounded captioning, and agent egocentric action understanding. By connecting visual observations with human actions, camera motion, game states, and events at scale, EgoCS-400K serves as a practical bridge between passive web videos, controllable game simulation, and costly real-world embodied data.