Online Neural Space Time Memory for Dynamic Novel View Synthesis
Online novel view synthesis from multi-view streaming videos faces a fundamental trade-off: maintaining a persistent, long-horizon memory to reconstruct temporarily occluded regions while operating under strict real-time constraints. While Test-Time Training (TTT) offers a powerful memory mechanism, standard models mandate gradient-based memory updates at every frame to adapt to the changing motion in dynamic scenes. The computational cost of heavy memory updates precludes real-time application and can lead to instability over long contexts. Given that memory updates are more demanding than me
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- FuzzyOverlapping authors or contributors · 62%google-research/google-research →
“Shared author/contributor keys: sun”
- FuzzyOverlapping authors or contributors · 62%sgl-project/sglang →
“Shared author/contributor keys: luo”
- LinkedLinked via arxiv author · 85%Baback Elmieh →
“Online Neural Space Time Memory for Dynamic Novel View Synthesis”
- LinkedLinked via arxiv author · 85%Lynn Tsai →
“Online Neural Space Time Memory for Dynamic Novel View Synthesis”
- LinkedLinked via arxiv author · 85%Zeman Li →
“Online Neural Space Time Memory for Dynamic Novel View Synthesis”
- LinkedLinked via arxiv author · 85%Srinivas Kaza →
“Online Neural Space Time Memory for Dynamic Novel View Synthesis”
- LinkedLinked via arxiv author · 85%Tiancheng Sun →
“Online Neural Space Time Memory for Dynamic Novel View Synthesis”
- LinkedLinked via arxiv author · 85%Gabor Csapo →
“Online Neural Space Time Memory for Dynamic Novel View Synthesis”