Render, Don't Decode: Weight-Space World Models with Latent Structural Disentanglement
Training world models on vast quantities of unlabelled videos is a critical step toward fully autonomous intelligence. However, the prevailing paradigm of encoding raw pixels into opaque latent spaces and relying on heavy decoders for reconstruction leaves these models computationally expensive and uninterpretable. We address this problem by introducing NOVA, a world modelling framework that represents the system state as the weights and biases of an auxiliary coordinate-based implicit neural representation (INR). This structured representation is analytically rendered, which eliminates the decoder bottleneck while conferring compactness, portability, and zero-shot super-resolution. Furth...
| Authors | Roussel Desmond Nzoyem, Mauro Comi |
|---|---|
| Primary category | cs.CV |
| Published | 7 May 2026 |
| Last updated at source | 8 May 2026 |
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