World models are a powerful paradigm in AI and robotics, enabling agents to reason about the future by predicting visual observations or compact latent states. The 1X World Model Challenge introduces an open-source benchmark of real-world humanoid interaction, with two complementary tracks: sampling, focused on forecasting future image frames, and compression, focused on predicting future discrete latent codes. For the sampling track, we adapt the video generation foundation model Wan-2.2 TI2V-5B to video-state-conditioned future frame prediction. We condition the video generation on robot states using AdaLN-Zero, and further post-train the model using LoRA. For the compression track, we train a Spatio-Temporal Transformer model from scratch. Our models achieve 23.0 dB PSNR in the sampling task and a Top-500 CE of 6.6386 in the compression task, securing 1st place in both challenges.
@article{arxiv.2510.07092,
title = {Generative World Modelling for Humanoids: 1X World Model Challenge Technical Report},
author = {Riccardo Mereu and Aidan Scannell and Yuxin Hou and Yi Zhao and Aditya Jitta and Antonio Dominguez and Luigi Acerbi and Amos Storkey and Paul Chang},
journal= {arXiv preprint arXiv:2510.07092},
year = {2025}
}
Comments
6 pages, 3 figures, 1X world model challenge technical report