Beyond Sparse Rewards: A New Benchmark and Structure-Aware Graph Alignment for Micro-Drama Understanding
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arXiv:2604.17422v2 Announce Type: replace Abstract: Long video understanding remains a formidable challenge for Multimodal Large Language Models (MLLMs) due to the prohibitive cost of processing dens...
Short dramas, with their rapid shot rhythms, dialogue-driven focus shifts, and demanding cinematographic grounding, pose challenges that prompt-level or text-only video generation pipelines struggle to meet. We study plot-to-short-drama generation, where a global plot and local context are transformed into visually grounded multi-shot videos.
MVRCap is a new video captioning framework that treats caption generation as a reinforcement learning problem guided by structured verification. It introduces a Fine‑Grained Verifiable Reward system built on three pillars—Hierarchical Concept Coverage, Factuality Verification, and Conciseness Constraint—supported by the newly created MVRCap‑QA and MVRCap‑SFT datasets. Experiments show that MVRCap outperforms existing methods on benchmarks such as VDC, CAPability, and VC4VG.
arXiv:2606. 24107v1 Announce Type: cross Abstract: Short dramas, with their rapid shot rhythms, dialogue-driven focus shifts, and demanding cinematographic grounding, pose challenges that prompt-level or text-only video generation pipelines struggle to meet.
arXiv:2605.22144v2 Announce Type: replace Abstract: Existing approaches for digital short-drama production typically rely on one-shot LLM generated scripts and loosely coupled pipelines, which fail t...
arXiv:2608.29711v1 Announce Type: new Abstract: While MLLMs have made significant strides in chart comprehension and video understanding, current evaluations largely isolate these capabilities, leavi...