Paper review · October 14, 2026
VIS-Ground: Interactive Video Storytelling Through Contextual Grounding
VIS-Ground: Video Interactive Storytelling with Contextual Grounding
The method organizes source material, already generated video and viewer requests into a dependency model and turns it into generation constraints, raising the overall score by an average of 10.3 points across all three video generation backbones.
Problem
When viewers can intervene mid-generation, a new request must be consistent with both the source material (a story or knowledge document) and the state of the video already rendered. These dependencies are not visible in any single input. So feeding a request directly to the generator easily produces state contradictions or content that does not match the source.
Method
It has three stages. ① Structured Context Abstraction: a VLM extracts the states of the video, story and source as structured facts and derives dependencies between inputs (causality, temporal order, state transitions, etc.). ② Generation Constraints Induction: only the dependencies relevant to a candidate script are activated as constraints and judged as pass/fail/unknown. ③ Constrained Video Generation: planning, verification, revision and rendering are repeated, and the rendered video is verified again and corrected. For evaluation, the authors built VIS-Bench based on CoQA (250 sources, 1,004 interaction instructions, 2,008 instances).
Results
- It achieved the highest overall score on all three backbones: Omni-1.1-Flash, Veo 3.1 and MiniMax-H3. It improved by an average of 10.3 points (absolute) over the strongest baseline for each backbone.
- Per-backbone improvements were 12.3 points for Omni, 15.3 points for Veo and 3.4 points for MiniMax.
- The authors reported improvements in both story-based and knowledge-based grounding settings.
- In a formative study with 9 participants, the 7 who passed an attention check showed an average learning gain of 47.9 percentage points with interaction and 32.1 percentage points without.
Limitations
The semantic metrics rely on Gemini 3.8 Flash and Claude Opus 5 as automatic evaluators. Video quality (VQ) is based on sampled frames, so motion and audio are not evaluated. The formative study has a very small sample and is exploratory. The authors note that faithfully implementing the dependency extraction and rendering stages is still a challenge. The benchmark is synthetic data built from CoQA passages.
Why it matters
It shows a structured way for interactive or educational video services to follow user requests without contradicting earlier content or source facts. The method is not tied to a specific generation model.
Key terms
- Contextual Grounding
- The process of turning different inputs, such as source material, already generated video and new requests, into constraints the generation model must follow.
- Candidate-conditioned constraints
- Constraints that are not always applied to every dependency but are activated only when a candidate script introduces a relevant event.
- Plan–Verify–Revise–Render
- An iterative loop that plans a script, verifies and revises it against constraints, renders it, and then verifies the resulting video again.
Original
Authors: Bingxuan Li, Yiwen Song, Xueqing Wu, Yanzhou Pan, Yang Li, Kuang Su, Jingyun Liu, Sebastian Ko, et al. · Posted 2026-10-07 · License CC BY 4.0 · Based on the full text
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