arXiv Computer Vision
SlotMem: Character-Addressable Internal Memory for Narrative Long Video Generation
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Weave Forcing: Compositional Memory Routing for Interactive Long Video Generation
Weave Forcing is a training‑free framework that enhances interactive long video generation by enabling compositional memory routing. It uses an LLM to split user prompts into character and background slots, then applies masked memory weaving with semantic masks to selectively retrieve relevant historical references. The method also introduces coverage‑adaptive RoPE to adjust temporal offsets based on reference coverage, reducing visual artifacts and improving cross‑shot consistency while preserving visual quality and text alignment.
arXiv Machine Learning
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Addressable Memory for Video World Models
arXiv:2608. 07408v1 Announce Type: cross Abstract: We study visual persistence in interactive video world models.
arXiv Computer Vision
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PREM: Prefix-Steered Recurrent Memory for Long-Video Understanding
arXiv:2609.23601v1 Announce Type: new Abstract: Long-video understanding must capture transient visual evidence under strict token budgets, yet existing methods compress frames, append memory tokens,...
arXiv Computation and Language
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MemoryCard: Topic-Aware Multi-Modal Clue Compression for Long-Video Question Answering
MemoryCard is a video-memory-based augmentation framework designed to improve long-video question answering for Vision‑Language Models. It segments lengthy videos into semantically coherent units—each representing a distinct topic or event—by performing a self‑reading process over the video and aligned utterances. For each unit, the framework generates an event‑level video gist and selects representative visual moments, which are compiled into unified Memory Cards that are used for retrieval and answering questions, yielding up to a 21.8% relative accuracy improvement under comparable visual‑token budgets.
arXiv AI
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SEAM: Shot Entity-Attribute Memory for Consistent Short-Drama Generation at Scale
arXiv:2608.22725v1 Announce Type: new Abstract: Short-drama generation has grown into a large, industrialized pipeline, and as it scales from isolated shots to the episode level, visual continuity ha...
arXiv Machine Learning
5d ago
GROME: GROunding Factorized MEmory into Video World Models
GROME introduces a factorized memory framework for video world models that separates historical evidence into static and dynamic components. It uses local memory retrieval to align static history spatially and injects the factorized memory into models without extra training. Experiments show improved long‑horizon scene persistence and dynamic entity fidelity while maintaining visual quality and controllability.