arXiv AI
SEAM: Shot Entity-Attribute Memory for Consistent Short-Drama Generation at Scale
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Hugging Face Trending Papers
Aug 24
SEAM: Shot Entity-Attribute Memory for Consistent Short-Drama Generation at Scale
Short-drama generation has grown into a large, industrialized pipeline, and as it scales from isolated shots to the episode level, visual continuity has become a critical bottleneck. Current agent fra...
arXiv Computer Vision
Sep 30
Complementary Retrieval-Augmented Prompting for Consistent Long-Form Video Generation
arXiv:2609.37407v1 Announce Type: new Abstract: While recent video foundation models excel at generating high-quality short videos, long-form video generation remains a critical challenge, where a ma...
arXiv AI
Oct 5
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character and background slots, then applies masked memory weaving with semantic masks to selectively retrieve relevant historical references.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 AI
Jul 16
GroundShot: Visually Consistent Multi-Shot Long Video Generation via Entity-Grounded Shot Scheduling
arXiv:2606. 20799v2 Announce Type: replace-cross Abstract: Generating visually consistent multi-shot videos remains an open challenge.
Hugging Face Trending Papers
Aug 27
Thinking on Shots: Consistent Multi-Shot Video Editing with Agentic Reasoning
The paper introduces the Multi-Instruction Multi-Shot Long-Video Editing (MMLVE) task, aiming to edit long videos with multiple instructions while maintaining consistency across shots, decoupling instructions, and preserving spatiotemporal structure. It proposes an agentic editing framework that combines Large Language Models and Vision‑Language Models for shot-level decoupling and precise instruction parsing. A new dataset, MMLVE‑Bench, and three evaluation metrics are created to benchmark this task, and experiments show the proposed MMLVE‑Agent outperforms existing closed‑source state‑of‑the‑art methods by eliminating hallucinations and ensuring seamless transitions.
arXiv Computer Vision
Aug 28
Thinking on Shots: Consistent Multi-Shot Video Editing with Agentic Reasoning
The paper introduces the Multi-Instruction Multi-Shot Long-Video Editing (MMLVE) task, aiming to enable consistent editing of long videos with multiple instructions. It proposes an agentic editing framework that combines Large Language Models and Vision-Language Models for shot-level decoupling and precise instruction parsing. The authors also present the MMLVE-Bench dataset and evaluation metrics, showing that their MMLVE-Agent outperforms existing state‑of‑the‑art methods by eliminating hallucinations and preserving temporal consistency.