arXiv AI
Jul 13
Video Generation Models are General-Purpose Vision Learners
arXiv:2607. 09024v1 Announce Type: cross Abstract: Driven by next-token prediction, NLP shifted from task-specific models into powerful generalist foundation models.
DiVid: Diagnosing Dimension-Specific Diversity Collapse in Video Generation Models
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arXiv AI
Jun 10
Can Image Models Imagine Time? ImageTime: A Novel Benchmark for Probing Visual World Modeling Through Spatiotemporal Consistency
arXiv:2606. 10620v1 Announce Type: cross Abstract: Image generation models now produce high-quality static images, yet their ability to represent how a visual world changes over time remains poorly understood.
arXiv Computer Vision
Sep 25
WanPE: Towards Cinematic Prompt Enhancement for Modern Text-to-Video Generation
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 Computer Vision
6d ago
Auteur: Language-Driven Cinematographic Framing for Human-Centric Video Generation
Auteur is a language‑driven method that generates human‑centric camera framing for generative video models. It treats shots as framings relative to an actor, encoding shot size, angle, and composition as functions of human pose and motion, and uses a domain‑specific language that converts to standard 6‑DoF camera parameters. A fine‑tuned multimodal large language model acts as a virtual director, mapping natural language descriptions and coarse human motion to sparse DSL keyframes that are interpolated into continuous camera trajectories for video generation.
arXiv Computer Vision
Aug 25
Long-Horizon Audio-Visual Generation for Persistent Stories and Interactive Worlds
arXiv:2608.23383v1 Announce Type: new Abstract: Video generation is progressing beyond isolated clips toward long-form narratives and interactive worlds, requiring models to preserve identities, foll...
arXiv Computer Vision
4d ago
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 Computer Vision
3d ago
FrameMorrow: Future-guided Frame Selection with Prospective Tokens for Long-Horizon Video Generation
arXiv:2609.38839v1 Announce Type: new Abstract: Long-horizon video generation requires models to effectively leverage an increasingly long generation history. As the generated history grows, retainin...
arXiv Computer Vision
Sep 24
The Past Frames the Future: Memory for Autoregressive Video Generation
arXiv:2609.28466v1 Announce Type: new Abstract: Advances in generative models have improved video fidelity, enabling long-horizon generation, interactive world modeling, and evolving visual environme...
arXiv Computer Vision
2d ago
VTR-Bench: A Systematic Benchmark for Evaluating Visual Text Rendering in Video Generation
VTR-Bench is a new benchmark designed to evaluate how well video generation models render text within scenes. It includes 300 prompts across five real-world scenarios such as advertisements and scientific videos, and uses an automated pipeline with human alignment to assess text fidelity and scene/motion requirements. Experiments on 11 state‑of‑the‑art models show that even the best performer has a word error rate of 0.250, underscoring widespread challenges in visual text rendering.
arXiv Computer Vision
Aug 21
OmniCamera: A Unified Framework for Multi-task Video Generation with Arbitrary Camera Control
arXiv:2604. 06010v2 Announce Type: replace Abstract: Video fundamentally intertwines two crucial axes: the dynamic content of a scene and the camera motion through which it is observed.
Hugging Face Trending Papers
Aug 12
Beyond Trial-and-Error: Agentic Optimization for Image-to-Video Adherence
Modern black-box Image-to-Video (I2V) models offer powerful capabilities in automated content creation, yet their lack of fine-grained control and reliability presents significant challenges in professional workflows. Their inherent stochasticity causes minor variations in textual prompts or hyperparameters to yield drastically different outputs often necessitating inefficient, brute-force trial-and-error processes.
arXiv AI
2d ago
Video Generation Models: A Survey of Post-Training and Alignment
arXiv:2610.00812v1 Announce Type: cross Abstract: Video generation has rapidly progressed from short, low-quality clips to high-resolution, long-duration sequences with complex spatiotemporal dynamic...