arXiv Computer Vision
Hy-Nomas: Towards Free-form Interactive World Generation
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arXiv AI
Sep 2
H3-World: Turning Language Understanding into World Control
arXiv:2609.01560v1 Announce Type: cross Abstract: We present H3-World, an efficient framework that turns the 33B MiniMax-H3 video generator into an interactive world model. Our key finding is that, a...
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
Aug 24
OmniAssistBench: Assistant-style Interaction Benchmark for Omni-LLMs
OmniAssistBench is a new benchmark for evaluating omni-modal large language models (Omni-LLMs) as real‑time video assistants that actively guide users toward goals. The benchmark addresses the challenge of dynamic interaction paths by providing models with predefined priors from source videos, forcing them to follow the same routes as users. The dataset was constructed by reverse‑engineering existing Internet videos into multi‑turn clips, a process that required over 1,000 expert person‑hours. Results show that proprietary Gemini‑3‑Pro scores 66.4/100 while open‑source Qwen3‑Omni‑Instruct scores 51.2, revealing that current models often give incorrect or incomplete answers, struggle with visual prompts, and fail to maintain context or delay responses until target events.
arXiv AI
Sep 10
Kairos: A Dataset for Fine-Grained Video-Language Modeling over Space, Time, and Dynamics
Kairos is a new video dataset designed for fine-grained video-language modeling, featuring long-duration videos from ten minutes to half an hour. Each video is annotated with time-resolved labels that capture ongoing actions, entity appearances, attributes, interactions, and evolving contextual cues throughout the timeline. The dataset supports fine-grained evaluation, long-range modeling, reasoning, instruction data construction, representation learning, and video generation.
arXiv Computer Vision
2d ago
World-in-Context: Interactive Video World Modeling with In-Context Conditioning
arXiv:2610.33169v1 Announce Type: new Abstract: Interactive video world models require coordinated control over viewing perspectives, character actions, and camera motion. Existing methods typically...
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...