World-action models (WAMs) jointly model future observations and actions, typically predicting the future as RGB images. Other visual modalities such as depth, pretrained visual features, and point tracks can more efficiently capture geometric, semantic, and motion features. However, how best to combine these modalitie...
Adam Hung, B. Duisterhof, D. Ramanan et al.· 0 citations
World models endow perceptual systems with the ability to predict how scenes evolve under interaction. They are most beneficial when trained on diverse volumes of data, to instill a rich prior into downstream applications. Existing methods typically require robot action labels to learn action-conditioned 3D dynamics, w...
B. Duisterhof, Kai-Feng Zhang, Adam Hung et al.· 0 citations
Generating complete 3D scenes from sparse, unconstrained views is a fundamental challenge in 3D vision which requires reasoning beyond observed content while remaining computationally tractable. Existing feed-forward reconstruction methods are inherently limited to content visible in the input images, while 3D generati...
Thomas Lucas, Maxime Pietrantoni, Philippe Weinzaepfel et al.· 1 citation
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