From Video Clips to Creation Trajectory: Sora100K for AI-Native Video Creation

arXiv:2610.11770 · cs.CV · Submitted 2026-10-08 · Read on arXiv

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

Tom: I'm Tom, and with me are Jane, Lu, senior AI researcher at Tsinghua, Meng, lead engineer at a mysterious AI startup and Lalam, the in-house Large Language Model.

Jane: Today's paper: "From Video Clips to Creation Trajectory".

Tom: The gist: Sora100K introduces a dataset that represents AI-Native video creation as a structured video creation trajectory, connecting generation roots, editing edges,

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

Title and authors: Tom: Let's talk about who wrote this, Sicong Yang and Ruihuan Yang as the main authors. They’ve put together Sora100K to address this trajectory concept. It really shows they're focused on building a dataset that reflects the actual AI-Native workflow.

Jane: And when we look at the full title, "From Video Clips to Creation Trajectory: Sora100K for AI-Native Video Creation," it’s telling us the core contribution is moving past just clips to capture this whole process. It’s about capturing the iterative nature of how people use AI right now.

Lu: This representation connects the generation roots, successive editing edges, and a final video in one structure, which preserves things like source-to-edit lineage and editing order. It’s a big structural change for how we organize multimodal data.

Meng: So when you look at the comparison in Table one on page two they show how existing datasets are mostly separated into video generation or editing records, but Sora100K links them together as trajectories <ref:2610.11770#pg1>. That’s a key distinction for training models that need to understand sequential editing instructions.

Lalam: It shows that the existing open academic datasets remained largely video clips, but Sora100K organizes diverse video creation trajectories into those three structural subsets. That means we're getting something much more connected than just static pairs of text and video.

The paper's summary: Tom: So to summarize the paper, they constructed Sora100K, a dataset with over 100K video records from the AI-Native platform Sora2 <ref:2610.11770#pg2>. They’ve done a lot of work on data curation, filtering for quality using things like accessibility and VBench quality metrics.

Jane: They didn't just throw videos together; they built a systematic pipeline that ensures traceability by organizing them according to recovered source-to-edit relations. The annotations are also hierarchical, providing root-level semantic annotations for generation roots, edge-level operation annotations for editing edges, and trajectory-level structural annotations for multi-turn video editing.

Lu: This annotation process is scaled using Qwen2 point 5 VL7B to help annotate the semantics of the generation and editing operations, with human inspection verifying those automated labels to make sure everything is accurate <ref:2610.11770#pg1>. It’s a lot of work just to get that quality control in place.

Meng: The dataset characteristics are also important for practical use; they preserve two properties that aren't very explored in open datasets: multi-shot structure and audio-visual synchronization. Most videos are about ten seconds long and have around three hundred frames at thirty FPS, with about sixty-five point five percent exhibiting a multi-shot structure.

Lalam: That focus on preserving multi-shot structure and audio-visual synchronization is what makes it closer to the actual real-world video creation workflow we see today, which is much more complex than just one simple clip.

The paper's improvements: Tom: Now let’s talk about how Sora100K actually helps models perform better. They test this by applying lightweight LoRA adaptation to LTX-two models to see the supervision value it provides for video generation and editing tasks <ref:2610.11770#pg1>.

Jane: The results show that even a small subset of Sora100K already gives effective supervision, which improves visual quality, multi-shot generation, and cross-shot consistency compared to the original LTX-two pipeline <ref:2610.11770#pg1>. It’s not just better; it's demonstrably better in trajectory-level video creation.

Lu: Specifically, the Sora100K-adapted pipeline outperforms the original LTX-two pipeline in trajectory creation <ref:2610.11770#pg1>. The aesthetic quality, for example, its two-turn average goes from forty-one point five to forty-six point nine, while subject consistency stays comparable across those turns.

Meng: And when you look at the root level generation experiments, Sora100K gets the best overall score of seventy-six point four three and leads on five out of six metrics compared to the original model LTX-two <ref:2610.11770#pg2>. It even improves imaging quality from forty-seven point eight four to fifty-seven point two two for that specific experiment in page nine.

Lalam: For editing experiments, adaptation improves instruction following, Sinst, from forty-three point four to forty-seven point nine and aesthetic quality, SAes, from forty-nine point eight to fifty-three point eight when focusing on object-level operations which make up about half of the subset at page ten.

Conclusion: Tom: So wrapping this up, the main contributions are three things: first, structuring video creation as a creation trajectory with generation roots and editing edges. Second, building Sora100K with its semantic annotations and source-to-edit lineage. And third, providing that structured annotation and trajectory organization to support research on cross-turn dependencies.

Jane: It gives researchers a concrete way to study how things evolve across turns in an AI video workflow, which is really helpful for understanding the dependencies between those successive edits. This dataset and its annotations are publicly available on Hugging Face at https://huggingface.co/datasets/ysicong/Sora100K.

Lu: The structure they provide is what supports research on cross-turn dependencies, which is something that was missing in previous datasets where everything was just isolated clips. It allows you to study those connections directly within the data organization itself.

Meng: For practical AI development, this means we can build systems that handle iterative refinement much more effectively by leveraging these trajectory annotations during fine-tuning or adaptation. It makes the system more robust to successive editing instructions.

Lalam: I think what this means for culture is that we start seeing AI video creation move toward something where you guide the final look of a video through a series of deliberate, connected steps rather than just hoping for one perfect initial output.

Tom: Yeah, that’s the big picture here—we've moved from static snapshots to understanding the actual flow of creating content with AI. Thanks for tuning in.

Sicong Yang, Ruihuan Yang, Jian Lu, Jianfei Yuan, Xiaodong Cun, Xiuli Bi

Chongqing University of Posts and Telecommunications

cs.CV

Submitted: 2026-10-08

Updated: 2026-10-08

Comments: 18 pages, 17 figures

License: http://creativecommons.org/licenses/by/4.0/

Importance score: 88/100

The gist: The gist: Sora100K introduces a dataset that represents AI-Native video creation as a structured video creation trajectory, connecting generation roots, editing edges, and complete trajectories while

Key concepts

Video Creation Trajectory: This concept represents the entire path of an AI-Native video workflow, starting from a text prompt (generation root), moving through single edits (editing edges), and culminating in a complete, multi-turn video. It structures the video creation process into distinct stages rather than treating generation and editing as separate events.
Sora100K: This is a large dataset containing over 100,000 videos curated from Sora2, organized according to their structural role in the workflow. It explicitly preserves crucial elements like multi-shot structure and audio-visual synchronization, making it a foundation for training models on realistic video creation workflows.
Generation Roots and Editing Edges: These are the two primary structural subsets of Sora100K. Generation roots are the initial text-to-video records where AI creates the first video from a prompt. Editing edges are single-turn records that detail subsequent edits applied to that initial video, showing how one step leads to another.
Supervision Value Assessment: This refers to testing how much guidance or supervision a dataset provides when training models like LTX-2 for video tasks. By adapting these models with Sora100K, researchers found that the dataset significantly improves visual quality and consistency in multi-shot generation compared to using the original model alone.

Terminology

Consistency — 19× in this paper · explained in 3 episodes
Adaptation — 7× in this paper · explained in 1 episode
Transformer — 4× in this paper · explained in 2 episodes
Alignment — 3× in this paper · explained in 1 episode
Temporal consistency — 3× in this paper · explained in 2 episodes
Accuracy — 2× in this paper · explained in 1 episode
Benchmark — 2× in this paper · explained in 2 episodes
Diffusion Model — 2× in this paper · explained in 8 episodes
Diffusion Models — 2× in this paper · explained in 6 episodes
Attention Mechanism — 1× in this paper · explained in 2 episodes
Downstream Tasks — 1× in this paper · explained in 2 episodes
F1 Score — 1× in this paper · explained in 3 episodes
Fine-Tuning — 1× in this paper · explained in 5 episodes
Foundation Model — 1× in this paper · explained in 3 episodes
LoRA Adaptation — 1× in this paper · explained in 2 episodes
LoRA Adapters — 1× in this paper · explained in 3 episodes
LoRA Fine-Tuning — 1× in this paper · explained in 3 episodes
Transformer Architecture — 1× in this paper · explained in 2 episodes

Summary

The gist: Sora100K introduces a dataset that represents AI-Native video creation as a structured video creation trajectory, connecting generation roots, editing edges, and complete trajectories while preserving crucial properties like multi-shot structure and audio-visual synchronization.

Dataset Structure and Representation

Sora100K organizes diverse video creation trajectories into three structural subsets based on their roles in the workflow<ref:2610.11770#pg4>. Specifically, it identifies text-to-video generation records as roots, single-turn video editing records as editing edges, and multi-turn video editing records as complete trajectories<ref:2610.11770#pg5>. This representation connects the initial generation with successive editing operations and intermediate video states<ref:2610.11770#pg5>. The three subsets jointly cover different stages of video creation trajectories<ref:2610.11770#pg6>.

Data Curation and Annotation Pipeline

The construction of Sora100K involves a systematic pipeline that ensures data quality and traceability<ref:2610.11770#pg6>. This process includes data curation, quality filtering using accessibility, VBench-based quality, and traceability filtering<ref:2610.11770#pg6>. The dataset is organized according to recovered source-to-edit relations<ref:2610.11770#pg6>. Annotations are provided by using Qwen2.5-VL-7B to scale the annotation of generation semantics and editing operations, with human inspection verifying the accuracy of these automated annotations<ref:2610.11770#pg6>. The annotation process is hierarchical, providing root-level semantic annotations for generation roots, edge-level operation annotations for editing edges, and trajectory-level structural annotations for multi-turn video editing<ref:2610.11770#pg6>.

Key Dataset Characteristics

Sora100K preserves two properties that remain underexplored in open academic datasets: multi-shot structure and audio-visual synchronization<ref:2610.11770#pg6>. The dataset contains over 100K video records curated from the AI-Native social media platform Sora2<ref:2610.11770#pg6>. Beyond its trajectory-level organization, it explicitly preserves multi-shot structure and audio-visual synchronization, making it closer to contemporary real-world video creation workflow<ref:2610.11770#pg6>. The dataset statistics show that most videos are approximately 10 seconds long and contain around 300 frames at 30 FPS, with approximately 65.5% exhibiting a multi-shot structure<ref:2610.11770#pg6>.

Experimental Validation of Supervision Value

The utility of Sora100K is assessed by applying lightweight LoRA adaptation to LTX-2 models to evaluate the supervision value<ref:2610.11770#pg6>. Experiment results show that a small subset of Sora100K already provides effective supervision for video generation and editing, improving visual quality, multi-shot generation, and cross-shot consistency<ref:2610.11770#pg6>. Specifically, the Sora100K-adapted pipeline performs better in trajectory-level video creation than the original LTX-2 pipeline<ref:2610.11770#pg7>. The improvement comes primarily from aesthetic quality, whose two-turn average increases from 41.5 to 46.9, while subject consistency remains comparable<ref:2610.11770#pg7>. Furthermore, root-level video generation experiments show that Sora100K achieves the best overall score of 76.43 and leads on five of the six metrics compared to the original model<ref:2610.11770#pg9>. Edge-level video editing experiments demonstrate that adaptation improves instruction following (Sinst) from 43.4 to 47.9, and aesthetic quality (SAes) from 49.8 to 53.8<ref:2610.11770#pg9>.

Conclusion on Contributions

The main contributions of the paper are threefold<ref:2610.11770#pg6>. First, they introduce a representation that structures AI-Native video creation as a creation trajectory with generation roots and editing edges<ref:2610.11770#pg6>. Second, they construct Sora100K, a large-scale dataset with semantic and editing annotations, source-to-edit lineage, intermediate video states, multi-shot structure, and audiovisual synchronization<ref:2610.11770#pg6>. Third, they provide structured annotations and trajectory-level organization that support research on cross-turn dependencies<ref:2610.11770#pg6>. The dataset and supplementary materials are publicly available at https://huggingface.co/datasets/ysicong/Sora100K<ref:2610.11770#pg6>.

Limitations

The paper notes two limitations<ref:2610.11770#pg9>. First, suitable open-source models remain limited; many are derived from LTX-2 or incompatible with the input modalities<ref:2610.11770#pg9>. Second, creation trajectories remain beyond the capabilities of most open-source models, those are close-source<ref:2610.11770#pg9>.

Dataset Composition Details

The Text-to-Video Generation subset is dominated by people-related content, with the People category accounting for 76.85% of the subset<ref:2610.11770#pg10>. In the Single-Turn Video Editing subset, object-level editing accounts for the largest proportion at 52%, followed by style-level editing at 27%<ref:2610.11770#pg10>. The Multi-Turn Video Editing subset contains ordered video chains formed through successive editing operations, with chain length defined as the number of videos in an editing chain<ref:2610.11770#pg10>.

Annotation Reliability

The reliability of the VLM-based annotations used to characterize creation trajectories is supported by random sampling and comparison with human annotations<ref:2610.11770#pg7>. The automatic annotations achieve an accuracy of 98.19%, a Cohen’s κ of 0.959, and a weighted F1 score of 98.26%<ref:2610.11770#pg7>.

Training Configurations

For the text-to-video generation experiment, the base LTX-2 model is adapted using LoRA with a rank of 32<ref:2610.11770#pg6>. For the single-turn video editing experiments, LTX-2-T2AV-IC is fine-tuned using LoRA with a rank of 32<ref:2610.11770#pg6>. Both experiments are conducted on a single NVIDIA H100 GPU with approximately 40 GB of peak GPU memory usage<ref:2610.11770#pg6>.

Qualitative Results

The Sora100K-adapted model yields more consistent subjects and fewer structural artifacts across shots compared to LTX-2<ref:2610.11770#pg9>. The models sequentially modify the source video according to four editing instructions, and the Sora100K model better preserves character appearance and scene structure while accumulating changes across turns<ref:2610.11770#pg9>. The Sora100K-adapted pipeline achieves higher aesthetic quality while maintaining comparable subject consistency across successive editing turns<ref:2610.11770#pg9>. The model better preserves the visual content and previous editing effects across successive turns when applying a high-motion anime style and adding a halo to the character<ref:2610.11770#pg9>.

Dataset Samples Categories

The Text-to-Video Generation subset includes eight semantic categories: People, Animal, Plant, Food & Drink, Transportation, Scenery, Architecture, and Science & Tech<ref:2610.11770#pg6>. The Single-Turn Video Editing subset covers five major groups of editing operations: Object-level editing (52%), style-level editing (27%), scene-level editing (9%), narrative and temporal editing (5%), and Other operations (7%)<ref:2610.11770#pg10>. The Multi-Turn Video Editing subset captures iterative creation processes through ordered video chains<ref:2610.11770#pg10>.

Prompt Length Distribution

Generation prompts are divided into five length ranges, with prompts containing no more than 10 words accounting for 52.

Improvements for AI systems

Improved AI system can perform iterative, multi-turn video creation by utilizing Sora100K as a structured dataset representing video creation trajectory. This allows the system to connect the initial generation with successive editing operations and intermediate video states, enabling complex refinement of visual style, camera motion, and scene adjustments through natural language instructions.
The system can achieve higher aesthetic quality in iterative refinement by adopting the Sora100K-adapted pipeline, as shown in Table 4: adaptation improves Sinst from 43.4 to 47.9, Seff from 38.7 to 44.2, and SAes from 49.8 to 53.8. This directly enhances the visual outcome of successive editing turns compared to base models like LTX-2 (Base).
The system can generate superior initial video states by leveraging generation root supervision, as evidenced in Table 3: Sora100K achieves the best overall score of 76.43 and leads on five of the six metrics when adapted to LTX-2, specifically improving imaging quality from 47.84 to 57.22.
The system can ensure stronger instruction following during editing operations by utilizing edge-level supervision, as shown in Table 4: adaptation improves Sinst from 43.4 to 47.9, Seff from 38.7 to 44.2, and SAes from 56.0 to 59.4. This strengthens transitions between successive video states by focusing on object-level operations (which account for the largest proportion at 52% of the subset).
The system can maintain subject consistency across shots during multi-turn editing by utilizing trajectory-level annotations, which preserves source-to-edit lineage, editing order, and intermediate states. This allows the model to better preserve character appearance and scene structure when applying multiple successive edits, as seen in Figure 16.

Abstract

AI-Native video creation is shifting from isolated video clips toward iterative video creation workflows. However, existing datasets remain largely video clips, representing video generation and editing as separate tasks rather than connected stages of a video creation workflow. In this paper, we introduce Sora100K, a dataset that represents the AI-Native video creation workflow as a structured video creation trajectory. Specifically, we first identify video creation trajectories and decompose them into three subsets according to their structural roles: text-to-video generation records as roots, single-turn video editing records as editing edges, and multi-turn video editing records as complete trajectories. Then, we use a VLM to assign semantic annotations for generation roots and editing-operation annotations for editing edges. A strict construction pipeline further reconstructs source-to-edit lineage, editing order, and intermediate video states while ensuring data quality. Finally, we perform lightweight adaptation on LTX-2 models to assess the supervision value of Sora100K. The results show improvements in visual quality, multi-shot generation, and cross-shot consistency, while successive-turn evaluation reveals that following multi-turn editing instructions remains challenging. Sora100K establishes a new data foundation for AI-Native video creation beyond isolated video clips and toward structured video creation trajectory. The dataset and supplementary materials are publicly available at https://huggingface.co/datasets/ysicong/Sora100K.