Video interactive storytelling enables viewers to actively steer how a video unfolds. However, once we allow viewers to intervene during generation, a new challenge arises: The viewer's request can ha...
WanPE is a 397‑B parameter prompt‑enhancement model that learns director‑level cinematic planning from 1.05 M real‑world videos. It generates shot‑level cinematic plans through video‑grounded reverse construction and uses Semantic‑Consistency GRPO (SC‑GRPO) to maintain user intent across shots and time. In evaluations, WanPE improves human preference over raw prompts by up to 50.86 points for 30‑second videos and outperforms commercial offerings for shorter durations.
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:2604. 25220v2 Announce Type: replace Abstract: Data videos combine animated visualizations with synchronized narration to communicate quantitative information and are widely used in journalism, education, and public communication.
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.
The paper introduces PEBench, a unified benchmark for evaluating prompt enhancers (PEs) in video generation across text-to-video, image-to-video, and reference-to-video tasks. It contains 1,100 expert-verified cases, 1,005 visual assets, and 35 fine-grained tasks, and employs a fact-extraction and rubric-based assessment framework covering 24 criteria. Evaluation of various PE methods shows a trend toward intent-preserving cinematic planning, with certain methods excelling in coverage or coherence, and human validation confirms that PEBench scores align with expert judgments and downstream video quality.
arXiv:2608.29621v1 Announce Type: cross Abstract: Long-horizon story-driven video generation requires a production agent to coordinate narrative decomposition, state tracking, shot design, prompt con...
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character and background slots, then applies masked memory weaving with semantic masks to selectively retrieve relevant historical references.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.
SemComp-Bench introduces a new video generation task called Semantic Task Completion, where a model must produce a video that achieves a specified outcome while maintaining semantic alignment with a reference image. The benchmark includes the SemComp-Data dataset, spanning six domains, and a four-stage curation pipeline that transforms raw videos into standardized instances. Evaluation is performed via a vision‑language model that answers structured binary questions, yielding Outcome Achievement (OA) and Generation Reliability (GR) scores.
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.
arXiv:2607. 15778v1 Announce Type: cross Abstract: Video Large Language Models (Video LLMs) have made significant advancements in various video understanding tasks.
arXiv:2606. 20388v1 Announce Type: cross Abstract: Data videos integrate dynamic charts, voice narration, and synchronized animations to communicate data insights as temporal narratives, making them an effective medium for improving data consumption efficiency in the data management lifecycle.