Knowledge-Intensive Video Generation

arXiv:2606.01285 · cs.CV, cs.AI · Submitted 2026-05-31 · Read on arXiv

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Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Today's paper: "Knowledge-Intensive Video Generation".

Jane: Text-to-video generation has rapidly improved in visual quality, but it remains under-evaluated for factuality and practical usefulness in information-seeking scenarios.

Tom: First, who's behind it and why it matters.

Paper summary: Tom: So we're diving into the paper "Knowledge-Intensive Video Generation," which looks at how current text-to-video models stack up when users actually need to *know* something from a video rather than just seeing something pretty. Jane, can you lay out the main idea for us?

Jane: Absolutely, Tom. The core idea here is introducing Knowledge-Intensive Video Generation or KIVI1 as a new way to test these models. Instead of giving them super long descriptions of what to show in a video, KIVI starts with short prompts that ask for things like explanations, procedures, or demonstrations.

Lu: That shift from detailed scene descriptions to short instructional prompts is really interesting because it forces the AI to make decisions about which specific content to include while making sure everything stays true to real-world knowledge. It moves us closer to how people actually seek information online.

Meng: I'm curious, what exactly is KIVI trying to prove? Is it just about making the video look better, or is it testing a different kind of capability?

Tom: It's definitely testing a different capability. The paper claims that visual quality alone isn't enough for practical use cases where people are seeking information from generated videos. They introduce automatic metrics for factuality and helpfulness to see if models can generate videos that are both accurate and useful based on short prompts.

Jane: Exactly. They build a benchmark called KIVIBench with one thousand eighty prompts covering different knowledge-seeking scenarios, and they propose FactP for factuality and HelpS for helpfulness as automatic ways to judge these videos.

Lalam: From my perspective as a model, the focus on these specific evaluation metrics suggests that improving how models handle verifiable claims and user needs is a path toward making AI more reliably useful in daily tasks.

Tom: And the results they share are pretty telling, showing that even state-of-the-art models still lag behind human performance when it comes to things like procedural operations and clear information presentation.

Lu: That lag in procedural operations is a big signal because it means the AI isn't just rendering scenes; it's not grasping the actual steps involved in doing something, which points toward deeper knowledge integration.

Jane: And to help measure this, they found that their proposed metrics for factuality and helpfulness align much better with what human evaluators think is right compared to older methods.

Meng: So, if we look at the findings regarding errors, the paper points out that entity misrepresentation is the most frequent issue, followed by incorrect procedures and component misplacement. That sounds like a very concrete set of problems for engineering to tackle.

Tom: It really is. And they did show that making the generation interactive, where it can correct its own mistakes through visual feedback, actually boosted both the factuality and helpfulness scores significantly.

Lalam: That interactive correction ability is significant because it shows a mechanism for self-correction that could be baked into the generation process to reduce cascading errors in complex instructions.

Jane: It seems like they are pointing toward a future where video generation isn't just about making things look realistic, but about building a system that can follow instructions accurately while maintaining factual integrity.

Tom: Moving on to the conclusion of this paper, the authors frame KIVI as a real evaluation setting for text-to-video generation that goes beyond just visual appeal. They really want us to see this as a challenge for creating videos that are instructionally useful, not just visually plausible.

Lu: The implication here is that the next generation of video AI needs to integrate more robust external knowledge retrieval mechanisms rather than relying solely on what's in the training data about visual style.

Meng: For us working on practical applications, this means we have a clearer target for improvement: we need to focus on improving entity-specific knowledge and component localization if we want to build tools that actually teach or demonstrate things correctly.

Jane: So, in simple terms, the paper is saying that for video AI to be truly useful for information seeking, it needs to become much better at understanding and executing instructions based on external knowledge rather than just being visually convincing.

Tom: That's a solid way to put it. The title "Knowledge-Intensive Video Generation" really captures the essence of what they are trying to achieve here—moving beyond surface-level plausibility toward genuine understanding.

Lalam: It suggests that the cultural impact will be seen when AI can reliably explain complex procedures or concepts in a video format, making learning and skill acquisition much more accessible.

Conclusion: Radio: Knowledge-Intensive Video Generation Segment**

Tom: So, we've been deep into this paper about Knowledge-Intensive Video Generation, and now it's time to wrap up our discussion on what this all means for the future of video creation. Jane, can you give us a quick rundown on the title and who penned this research?

Jane: Certainly, Tom. The title itself really sets the stage by focusing on making videos that require knowledge, not just looking pretty. The authors are trying to address a gap in current text-to-video models where they struggle with factual accuracy and being genuinely helpful for users seeking information.

Lu: I think the authors, given their background in complex knowledge tasks, really knew where the blind spots were in these systems before they even started this work. They weren't just looking at aesthetics; they were aiming for actual utility.

Meng: From an engineering standpoint, I see the title as a direct challenge to current methods that prioritize visual plausibility over instructional integrity. It suggests we need to build systems that can actually follow a prompt's intent, not just mimic a scene.

Lalam: For me, the implication is huge because if AI can reliably generate videos that explain procedures or answer questions accurately, it could fundamentally alter how people learn new skills and understand complex topics in the real world.

Tom: That’s a big picture for us to consider, Lalam. It sounds like this paper is pushing us toward building tools that are more like true tutors or instructional aids rather than just fancy image generators.

Jane: Exactly. The authors are demonstrating that we need metrics beyond just looking at the pixels; we need to measure how well the video delivers on the information requested by a user's prompt.

Lu: And they introduced these specific automatic metrics, FactP and HelpS, which are designed to align more closely with what human experts actually value in terms of correctness and usefulness.

Meng: Those metrics sound like a solid way to give us concrete targets for model training; it moves the goal from vague subjective feedback to quantifiable performance indicators for factuality and clarity.

Lalam: I’m really excited about the potential impact on culture because if these tools become this reliable, we could see a massive democratization of education and skill-building opportunities across all fields.

Tom: It really is something to get hyped about, Lalam. This research shows that the next big step for video AI isn't just better visuals; it’s deeper comprehension and better execution of complex tasks.

Jane: And it confirms that the challenge ahead is moving beyond visual plausibility to achieving genuine knowledge-based understanding in every video we create.

Lu: This leads us perfectly into what the authors suggest next—the future of this research involves prioritizing entity-specific knowledge and precise component localization for better results.

Fudan University · Shanghai Jiao Tong University

cs.CV, cs.AI

Submitted: 2026-05-31

Updated: 2026-09-28

Code: https://github.com/wcxhimself/KIVI

Importance score: 92/100

The gist: Text-to-video generation has rapidly improved in visual quality, but it remains under-evaluated for factuality and practical usefulness in information-seeking scenarios.

Key concepts

KIVI Task Setting
This is a new test where models generate videos based on short questions asking for explanations or demonstrations. It moves beyond simple scene descriptions by requiring the model to use world knowledge to decide what content to show, focusing on factual correctness and usefulness.
FactP (Factual Precision)
This metric measures how many verifiable claims in a generated video are actually correct. An LLM extracts these claims, which are then checked against world knowledge for accuracy. It quantifies the model's ability to convey true information rather than just plausible visuals.
HelpS (Helpfulness Score)
This metric assesses how useful a video is by having an LLM rate it on Relevance, Completeness, and Clarity. A higher score means the video provides relevant, complete, and easy-to-understand information to the user seeking help.
Entity Misrepresentation
This is a common failure type where models correctly depict an object but show it in the wrong physical location or context. It represents a significant error in spatial understanding and world knowledge when generating videos.

Terminology

Summary

Text-to-video generation has rapidly improved in visual quality, but it remains under-evaluated for factuality and practical usefulness in information-seeking scenarios. This work introduces knowledge-intensive video generation (KIVI1), a new task setting that evaluates whether models can generate factually accurate and useful videos from short information-seeking prompts, highlighting the need to move beyond mere visual plausibility.

The gist

Knowledge-intensive video generation (KIVI1) is a task setting where models generate videos from short information-seeking prompts that ask for explanations, procedures, or demonstrations, and the paper introduces automatic metrics for factuality and helpfulness that align better with human annotations than existing alternatives.

KIVI Task Setting and Benchmark

The KIVI task setting evaluates whether models can generate factually accurate and useful videos from short information-seeking prompts rather than fully specified scene descriptions. This setting is designed to reflect practical use cases where users seek information through generated videos, starting from a short instructional prompt and requiring the model to decide what content to show while ensuring events, objects, and actions are faithful to relevant world knowledge. To evaluate models on KIVI, the authors construct KIVIBench, a benchmark of 1,080 prompts covering diverse knowledge-seeking scenarios. This benchmark is constructed through a pipeline involving manual construction of seed prompts based on five quality criteria—including ensuring the prompt is factually correct and involves uniquely identifiable entities with easily accessible documentation for verification—followed by LLM expansion and rigorous two-stage quality control to arrive at the final set of 1,080 prompts.

Automatic Evaluation Metrics

The paper proposes two complementary automatic metrics to evaluate generated videos: FactP (Factual Precision) and HelpS (Helpfulness Score). FactP measures factuality by estimating the fraction of verifiable claims conveyed in the generated video that are factually correct, following the intuition of claim-level factual precision in long-form text generation. Claims are extracted by an LLM using a specific prompt, and each is then verified against world knowledge and classified as “Correct”, “Incorrect” or “Uncertain.” HelpS measures helpfulness by having an LLM review the video and rate it along three dimensions: Relevance, Completeness, and Clarity, each on a scale from 0 to 10. The final score is computed as HelpS = Rel. + Compl. + Clar. × 100%. The authors validate these metrics through human evaluation, finding that they achieve stronger agreement with human annotations than existing alternatives for both factuality and helpfulness.

Model Benchmarking and Failure Analysis

The authors benchmark seven state-of-the-art video generation models, including closed-source systems like Seedance 2.0 and HappyHorse 1.0, and open-source models such as Wan 2.2 and HunyuanVideo 1.5, comparing them with human performance on KIVI-Bench. Experiments show that current systems still lag behind human performance, especially on visual properties, procedural operations, and clear information presentation. Detailed analysis of incorrect claims reveals recurring failure patterns: Entity Misrepresentation (the most frequent error), Incorrect Procedure (where the entity is rendered correctly but operated improperly), and Component Misplacement (where the correct component appears in the wrong physical location). These three error types account for over 98% of incorrect claims. Furthermore, interactive script generation was shown to improve performance, with interactive generation improving FactP by 3.2 points and HelpS by 8.2 points due to its ability to correct cascading factual errors through visual feedback.

Limitations and Future Directions

The work acknowledges several limitations, including the reliance on LLM-based claim extraction and verification pipelines which may inherit errors from the underlying LLMs, and the fact that the primary factuality metric is text-based, potentially missing errors requiring visual grounding. The multi-modal ablation suggests that directly verifying claims with images or videos remains challenging due to visual evidence ambiguity. The authors conclude that future work should prioritize entity-specific visual knowledge, procedural knowledge, and component localization to improve factual and instructionally useful video generation beyond just visual quality.

Key Contributions

The main contributions include: 1) Formulating KIVI as a new task setting for evaluating text-to-video generation beyond visual quality; 2) Constructing KIVI-Bench, a benchmark of 1,080 knowledge-intensive prompts; 3) Introducing automatic factuality and helpfulness metrics that better agree with human annotations; and 4) Benchmarking seven state-of-the-art models to show that KIVI remains challenging for current methods.

References

(The paper lists numerous references, including work on diffusion models, multi-modal knowledge tasks like OK-VQA, and video generation benchmarks.) (Note: Specific citations are not quoted in the summary as per the instructions focusing on substance and structure.) (The code and supplementary materials are available at https://github.

Improvements for AI systems

Based on the provided paper, here are specific, actionable improvements for AI systems and what those improved systems could achieve:

  1. A shift from purely visual quality metrics to a dual-metric evaluation framework focusing on utility and truth.

  2. The development and deployment of a rigorous benchmark (KIVI-Bench) specifically designed for knowledge-intensive video generation, moving beyond generic visual prompts to instructional/explanatory queries.

  3. The integration of LLM-based metrics (FactP and HelpS) into the model evaluation loop, allowing systems to be judged not just on how realistic they look, but on whether they provide correct and useful information for a user's query.

  4. A pipeline that utilizes an LLM-driven Outline Planner to decompose complex video requests into logical visual steps, ensuring procedural coherence before generation begins.

  5. An iterative script generation mechanism (Interactive Mode) that allows the AI model to self-correct factual errors in subsequent segments by referencing the terminal state of previous frames, reducing cascading errors.

  6. The implementation of a Claim Extraction and Verification module that automatically breaks down generated video content into atomic factual claims and cross-references them against external world knowledge (using LLMs) to assign verifiable truth values (Correct/Incorrect/Uncertain).

  7. A sophisticated error analysis system that classifies failures into specific categories—Entity Misrepresentation, Component Misplacement, or Incorrect Procedure—allowing developers to pinpoint exactly where the model's knowledge gap lies (e.g., needing better product-specific spatial knowledge vs. better procedural understanding).

This improved AI system can achieve the following:

  1. It will generate videos that are not only visually appealing but also demonstrably accurate and useful for instruction or explanation (e.g., How to set up X).

  2. The system will be able to distinguish between a video that looks realistic but is factually wrong (e.g., showing the wrong SIM card setup) and a genuinely helpful tutorial.

  3. It will exhibit improved procedural knowledge, as the iterative generation process prevents errors from cascading across segments, leading to more coherent demonstrations of complex tasks like assembling devices or performing repairs.

  4. It will demonstrate superior fine-grained visual reasoning by correctly rendering specific product features (e.g., the exact shape of a handle or the correct location of a component) and accurately executing specified steps, even when prompted with highly specific, non-trivial proper nouns.

  5. Researchers will gain a diagnostic tool to understand model weaknesses—knowing if a failure is due to hallucinating object appearances (Entity Misrepresentation), misunderstanding physical locations (Component Misplacement), or failing to execute the correct operational sequence (Incorrect Procedure).

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