Learning to Evolve Scenes: Reasoning about Human Activities with Scene Graphs
Understanding human behavior while interacting with the surrounding world is crucial for many applications of embodied AI. First-person videos are particularly informative for this problem, as they well capture how activities reshape the scene over time. However, existing approaches often rely on implicit visual or language-aligned representations, disregarding structured reasoning over the scene dynamic. We argue that explicit, compositional and editable representations of human-environment interactions can play a crucial role for rich grounded activity understanding. To this end, we introduc
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- LinkedLinked via arxiv author · 85%Francesca Pistilli →
“Learning to Evolve Scenes: Reasoning about Human Activities with Scene Graphs”
- LinkedLinked via arxiv author · 85%Simone Alberto Peirone →
“Learning to Evolve Scenes: Reasoning about Human Activities with Scene Graphs”
- LinkedLinked via arxiv author · 85%Giuseppe Averta →
“Learning to Evolve Scenes: Reasoning about Human Activities with Scene Graphs”
- PossiblePossibly related (embedding) · 47%typedef-ai/fenic →
- FuzzySimilar title/name (fuzzy) · 59%rasbt/reasoning-from-scratch →
“Fuzzy title match (0.73): “Learning to Evolve Scenes: Reasoning about Human Activities ” ≈ “rasbt/reasoning-from-scratch””
- FuzzySimilar title/name (fuzzy) · 59%aymericdamien/TopDeepLearning →
“Fuzzy title match (0.73): “Learning to Evolve Scenes: Reasoning about Human Activities ” ≈ “aymericdamien/TopDeepLearning””