English

WanToFight: Real-Time Generative Game Engine for Multi-Player Combat Interaction

Computer Vision and Pattern Recognition 2026-07-14 v1

Abstract

We present WanToFight, a generative game engine that simulates real-time, two-player The King of Fighters '97 (KOF~'97) gameplay from keyboard input. Prior generative game engines target either single-player first-person settings or non-real-time cooperative scenarios; multi-player control, real-time inference, complex physical interaction, and adversarial gameplay have not been jointly addressed. WanToFight closes this gap with three components built on the Wan-1.3B video diffusion transformer: a streaming autoregressive generator with block-causal attention and a rolling KV cache; a visually grounded Player Association module that binds each player's keyboard signal to a character identity; and a gated, locally causal keyboard injection module trained with a single-player-to-full-gameplay curriculum. A four-step DMD-distilled student paired with a pruned VAE decoder sustains 30FPS at 512x384 on a single NVIDIA RTX 5090 over the duration of a complete match. To our knowledge, WanToFight is the first generative game engine to combine multi-player control, real-time inference, complex physical interaction, and adversarial gameplay in one system.

Cite

@article{arxiv.2607.12592,
  title  = {WanToFight: Real-Time Generative Game Engine for Multi-Player Combat Interaction},
  author = {Li Hu and Guangyuan Wang and Peng Zhang and Bang Zhang},
  journal= {arXiv preprint arXiv:2607.12592},
  year   = {2026}
}

Comments

Project Page: https://humanaigc.github.io/wantofight/