arXiv AI
Jun 9
ViMax: Agentic Video Generation
arXiv:2606. 07649v1 Announce Type: cross Abstract: Long-form video generation requires systematic narrative planning and visual consistency that current short-clip methods cannot provide.
CineForge: Self-Improving Agents for Long-Horizon Video Generation
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arXiv AI
Jul 22
FilmWorld: Agentic Novel-to-Film Generation through Dynamic Cinematic World Modeling
arXiv:2607. 19038v1 Announce Type: cross Abstract: Translating novels into films poses a grand challenge for generative artificial intelligence, requiring conversion of abstract literary prose into long-form, multi-scene visual narratives.
arXiv AI
Jul 8
Prompt-Adapter Context Routing for Parameter-Efficient Multi-Shot Long Video Extrapolation
arXiv:2607. 06481v1 Announce Type: cross Abstract: We present PACR-Video, a parameter-efficient framework for multi-shot long video extrapolation that preserves recurring entities, scene structure, visual style, and causal progression without full generator fine-tuning.
arXiv AI
Aug 25
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...
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 AI
Jun 15
CineOrchestra: Unified Entity-Centric Conditioning for Cinematic Video Generation
arXiv:2606. 13768v1 Announce Type: cross Abstract: Cinematic video depicts multiple subjects acting or interacting at specific moments, captured with deliberate camera movement, and stitched together by shot transitions.
arXiv Computer Vision
Aug 25
ShotVerse: Advancing Cinematic Camera Control for Text-Driven Multi-Shot Video Creation
arXiv:2603.11421v2 Announce Type: replace Abstract: Text-driven video generation has democratized film creation, but camera control in cinematic multi-shot scenarios remains a significant block. Impl...
arXiv AI
Aug 10
DATAREEL: Automated Data-Driven Video Story Generation with Animations
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 Machine Learning
Aug 11
LAVE: Latent Visual Evidence-Enhanced Planning for Video Tool-use Agents
arXiv:2608. 07585v1 Announce Type: cross Abstract: Long-video understanding requires models to efficiently acquire and reuse sparse visual evidence from long and redundant video streams.
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.
arXiv Machine Learning
Jul 3
NarrativeTrack: Evaluating Entity-Centric Reasoning for Narrative Understanding
arXiv:2601. 01095v3 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) have achieved impressive progress in vision-language reasoning, yet their ability to understand temporally unfolding narratives in videos remains underexplored.