[2604.02330] ActionParty: Multi-Subject Action Binding in Generative Video Games

Computer Science > Computer Vision and Pattern Recognition arXiv:2604.02330 (cs) [Submitted on 2 Apr 2026] Title:ActionParty: Multi-Subject Action Binding in Generative Video Games Authors:Alexander Pondaven, Ziyi Wu, Igor Gilitschenski, Philip Torr, Sergey Tulyakov, Fabio Pizzati, Aliaksandr Siarohin View a PDF of the paper titled ActionParty: Multi-Subject Action Binding in Generative Video Games, by Alexander Pondaven and Ziyi Wu and Igor Gilitschenski and Philip Torr and Sergey Tulyakov and Fabio Pizzati and Aliaksandr Siarohin View PDF HTML (experimental) Abstract:Recent advances in video diffusion have enabled the development of "world models" capable of simulating interactive environments. However, these models are largely restricted to single-agent settings, failing to control multiple agents simultaneously in a scene. In this work, we tackle a fundamental issue of action binding in existing video diffusion models, which struggle to associate specific actions with their corresponding subjects. For this purpose, we propose ActionParty, an action controllable multi-subject world model for generative video games. It introduces subject state tokens, i.e. latent variables that persistently capture the state of each subject in the scene. By jointly modeling state tokens and video latents with a spatial biasing mechanism, we disentangle global video frame rendering from individual action-controlled subject updates. We evaluate ActionParty on the Melting Pot benchmark, dem...