TOWARD SPATIAL INTELLIGENCE AND PHYSICALLY REALISTIC WORLD MODELS

Scenethesis grounds language planning and visual guidance with explicit 3D assets and geometric and physical constraints, producing editable indoor and outdoor scenes with more coherent relations, support, and collision behavior. I-Scene instead repurposes a pretrained 3D instance generator as a feed-forward scene-level spatial learner. Learning from non-semantic random compositions reduces its dependence on annotated semantic layouts and supports generalization to unseen object arrangements.</p><p dir="ltr">Together, these works show that progress toward spatially intelligent and physically realistic world models depends not only on model design but also on how spatial experience is collected, structured, grounded, and reused.

High-quality multiview data supplies transferable observations, while inference-time and learning-based approaches expand interactive 3D environment generation beyond bounded datasets. These findings provide foundations for persistent, interactive, and physically grounded world models.</p>