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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Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
- Linked via arxiv authorYingtian Tang →
NEvo: Neural-Guided Evolutionary Video Synthesis for Dynamic Visual Selectivity
- Linked via arxiv authorSogand Salehi →
NEvo: Neural-Guided Evolutionary Video Synthesis for Dynamic Visual Selectivity
- Linked via arxiv authorMing Zhou →
NEvo: Neural-Guided Evolutionary Video Synthesis for Dynamic Visual Selectivity
- Linked via arxiv authorAmir Zamir →
NEvo: Neural-Guided Evolutionary Video Synthesis for Dynamic Visual Selectivity
- Linked via arxiv authorLeyla Isik →
NEvo: Neural-Guided Evolutionary Video Synthesis for Dynamic Visual Selectivity
- Linked via arxiv authorMartin Schrimpf →
NEvo: Neural-Guided Evolutionary Video Synthesis for Dynamic Visual Selectivity