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Displays of coordinated motion in bird flocks, fish schools, and human crowds are believed to emerge from local interactions between individuals. The crux of the problem thus lies in analyzing the local interactions and the neighborhood over which they operate. Most existing models attribute local interactions to metaphorical forces and assume that the positions and velocities of all neighbors are known; hence we refer to them as omniscient models. More realistic models would be based on the sensory information that actually governs local interactions in particular species. I will describe a bottom-up approach to building a visual model of pedestrian interactions and emergent collective behavior in human crowds, based on experiments in virtual reality, real crowd data, and multiagent simulations. The resulting model can account for collective motion, the ‘fundamental diagram’ of pedestrian dynamics, and pattern formation in crossing pedestrian flows. Collective behavior may thus be understood as emerging from the self-organizing dynamics of local visual interactions.