NEvo: Neural-Guided Evolutionary Video Synthesis for Dynamic Visual Selectivity
The human brain processes dynamic visual input through hierarchically organized, functionally specialized regions. While recent in silico brain encoding models can synthesize optimal stimuli to probe selectivity in different brain regions, prior work has been largely limited to static images, leaving dynamic visual processing underexplored. We introduce a novel neural-guided video synthesis framework that generates stimuli optimized for target brain regions across visual cortex. Our method performs evolutionary search over a structured prompt space, guided by a dynamic encoding model that pred
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- PossiblePossibly related (embedding) · 47%voxel51/fiftyone →
- LinkedLinked via arxiv author · 85%Yingtian Tang →
“NEvo: Neural-Guided Evolutionary Video Synthesis for Dynamic Visual Selectivity”
- LinkedLinked via arxiv author · 85%Sogand Salehi →
“NEvo: Neural-Guided Evolutionary Video Synthesis for Dynamic Visual Selectivity”
- LinkedLinked via arxiv author · 85%Ming Zhou →
“NEvo: Neural-Guided Evolutionary Video Synthesis for Dynamic Visual Selectivity”
- LinkedLinked via arxiv author · 85%Amir Zamir →
“NEvo: Neural-Guided Evolutionary Video Synthesis for Dynamic Visual Selectivity”
- LinkedLinked via arxiv author · 85%Leyla Isik →
“NEvo: Neural-Guided Evolutionary Video Synthesis for Dynamic Visual Selectivity”
- LinkedLinked via arxiv author · 85%Martin Schrimpf →
“NEvo: Neural-Guided Evolutionary Video Synthesis for Dynamic Visual Selectivity”
- FuzzyOverlapping authors or contributors · 62%sgl-project/sglang →
“Shared author/contributor keys: zhou”