Rethinking Visual Intelligence: Insights from Video Pretraining
Authors: name: Pablo Acuaviva, name: Aram Davtyan, name: Mariam Hassan, name: Sebastian Stapf, name: Ahmad Rahimi, name: Alexandre Alahi, name: Paolo Favaro
Organization: Computer Vision Group @ University of Bern

Large language models (LLMs) have demonstrated that large-scale pretraining
enables systems to adapt rapidly to new problems with little supervision in the
language domain. This success, however, has not translated as effectively to
the visual domain, where models, including LLMs, continue to struggle with
compositional understanding, sample efficiency, and general-purpose
problem-solving. We investigate Video Diffusion Models (VDMs) as a promising
direction for bridging this gap. Pretraining on spatiotemporal data endows
these models with strong inductive biases for structure and dynamics, which we
hypothesize can support broad task adaptability. To test this, we design a
controlled evaluation in which both a pretrained LLM and a pretrained VDM are
equipped with lightweight adapters and presented with tasks in their natural
modalities. Across benchmarks including ARC-AGI, ConceptARC, visual games,
route planning, and cellular automata, VDMs demonstrate higher data efficiency
than their language counterparts. Taken together, our results indicate that
video pretraining offers inductive biases that support progress toward visual
foundation models.