[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-8058b748-25c6-4863-917e-46e363773d07":3},{"id":4,"title":5,"summary":6,"original_url":7,"source_id":8,"tags":9,"published_at":23,"created_at":24,"modified_at":25,"is_published":26,"publish_type":27,"image_url":13,"view_count":28},"8058b748-25c6-4863-917e-46e363773d07","WanToFight 把视频扩散压成 30FPS 实时游戏引擎:多玩家格斗首跑通","视频扩散模型迈过了\"实时可玩\"这条硬门槛。arXiv 2607.12592 上挂出的 WanToFight,第一次把生成式游戏引擎的四件难事——多玩家控制、实时推理、复杂物理交互、对抗博弈——同时塞进一个系统。它的底座是阿里开源的 Wan-1.3B 视频扩散 Transformer,作者团队(Li Hu、Guangyuan Wang、Peng Zhang、Bang Zhang)在上面搭了三层增量架构,把\"按帧画画\"变成\"按键出招\"。\n\n第一层是流式自回归生成器,核心是 block-causal attention + rolling KV cache,把全局去噪切成因果块,显存占用恒定,长序列不再爆。第二层是 Player Association 模块,键盘信号先经过视觉 grounding 绑定到角色身份,再通过 gated、locally causal 的注入模块回到去噪网络,避免控制信号污染共享表征;训练上采用单人到全玩法的渐进课程,先学单人再学对抗。第三层是工程化的蒸馏栈:四步 DMD 蒸馏的学生模型 + 剪枝 VAE 解码器,把端到端时延压到 RTX 5090 单卡 512×384 @ 30FPS,能撑完一整场 KOF'97。\n\n把视角放高一点,WanToFight 的真正意义不在\"模型又多强\",而在它示范了一种工程范式:1.3B 规模 + 消费级单卡 + 蒸馏+KV cache+剪枝 VAE 三件套,视频扩散模型从此可以走出\"按秒计费\"的云端流水线,进入实时交互循环。这与 GameNGen、DIAMOND、Oasis 等单玩家\u002F第一人称路径形成了明显分野——多玩家对抗场景的视觉一致性和因果保持难度更高,WanToFight 第一次给出了实证答案。\n\n当然,512×384 分辨率、依赖单卡、格斗动作的离散空间相对简化,都意味着离\"真·3A 实时生成\"还有相当距离。但方向已经清楚:下一个阶段的视频扩散,不会再比谁的参数更多,而会比谁先把生成折进 30FPS 的交互回路。","https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.12592","7437aeb9-930c-4866-a2e9-48003c1a792b",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"7b67033c-19e6-4052-a626-e681bba64c7a","diffusion",null,{"id":15,"name":16,"slug":16,"description":13,"color":13},"0a93ec8e-ea39-4693-81de-563ca8c173f7","inference",{"id":18,"name":19,"slug":19,"description":13,"color":13},"b1853a5a-d940-42b7-94f9-0488ee3f2cf7","new-model",{"id":21,"name":22,"slug":22,"description":13,"color":13},"ebe5dcd1-46b1-4298-b8c2-8e0e2f456e56","video-generation","2026-07-15T16:05:00Z","2026-07-15T16:18:01.486665Z","2026-07-15T16:18:01.486672Z",true,"agent",4]