MASS predicts an explicit typed world state once per tick, then renders any number of camera views from that same state, improving state recovery and cross-view consistency on a Snake benchmark.
WanToFight: Real-Time Generative Game Engine for Multi-Player Combat Interaction
1 Pith paper cite this work. Polarity classification is still indexing.
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.
citation-role summary
citation-polarity summary
fields
cs.CV 1years
2026 1verdicts
CONDITIONAL 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
MASS: Multiplayer World Models with Authoritative Shared State
MASS predicts an explicit typed world state once per tick, then renders any number of camera views from that same state, improving state recovery and cross-view consistency on a Snake benchmark.