Year: 2025
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Authors: Mereu, Riccardo, Scannell, Aidan, Hou, Yuxin, Zhao, Yi, Jitta, Aditya, Dominguez, Antonio, Acerbi, Luigi, Storkey, Amos, Chang, Paul
Institutions: Aalto University, University of Edinburgh, University of Helsinki
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4415097635
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