PaperlayerView exact revision on arXiv

Source-linked AI summary

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

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

arXiv:2610.11770v1cs.CV

TL;DR

Existing datasets largely treat video generation and editing as isolated clips rather than connected creation trajectories. Sora100K organizes these stages into structured trajectories, and adaptation on the dataset achieves the best overall score on root-level generation among the compared models.

  • Problem

    Existing open academic datasets largely store independent generation pairs or single-step editing triplets, omitting connected trajectories, editing order, and intermediate video states.

    Source: Introduction · p. 2

  • Method

    Sora100K organizes text-to-video records as roots, single-turn edits as edges, and multi-turn edits as complete trajectories, with structured annotations and reconstructed lineage.

    Source: Introduction · p. 2, Conclusion · p. 7

  • Results

    76.43 overall score, with Sora100K-adapted LTX-2 leading on five of six root-generation metrics among the compared models.

    Source: Experiment Results · p. 6

  • Takeaways & Limitations

    Sora100K provides a curated resource for studying AI-Native video creation.

    Source: Conclusion · p. 7

    Creation trajectories remain beyond the capabilities of most open-source models.

    Source: Conclusion · p. 7

Abstract

from arXiv · show

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.

Introduction

AI-Native video creation increasingly involves successive natural-language edits, but existing datasets largely represent generation and editing as disconnected video clips. Sora100K structures this workflow as linked creation trajectories, preserving annotations, editing lineage, intermediate states, multi-shot structure, and audiovisual synchronization.

Motivation

  • Users increasingly refine video content, visual style, camera motion, and audio through successive natural-language editing instructions rather than a single generation or editing step.The paper frames this shift alongside advances toward cinematic videos with multi-shot structure and audiovisual synchronization.Source: Introduction · pp. 1–2
  • Existing text-to-video datasets store independent text–video pairs, while editing datasets store single-step triplets, omitting connected edits, their order, and intermediate video states.The cited examples are text-to-video datasets (Wang et al. 2025; Chen et al. 2024) and editing datasets (Qin et al. 2024).Source: Introduction · p. 2, Table 1 · p. 2

Dataset contribution

  • Sora100K contains over 100K records organized as generation roots, single-turn editing edges, and complete multi-turn video creation trajectories.The records are curated from the Sora2 platform and jointly cover different stages of creation.Source: Introduction · p. 2
  • Sora100K uses Qwen2.5-VL-7B to annotate generation semantics and editing operations, with manual inspection to verify automated annotations.Its construction preserves source-to-edit lineage, editing order, and intermediate video states.Source: Introduction · p. 2
  • Sora100K explicitly preserves multi-shot structure and audiovisual synchronization, properties the paper describes as underexplored in open academic datasets.The paper presents these characteristics as making the dataset closer to contemporary real-world video creation workflows.Source: Introduction · p. 2

Evaluation and intended use

  • Lightweight LoRA adaptation of LTX-2 shows that a small subset of Sora100K provides effective supervision for video generation.The dataset’s trajectory-level organization and structured annotations are intended to support cross-turn dependency research and potentially long-horizon video generation and editing.Source: Introduction · p. 2

Related Work

Video-generation and editing methods have advanced in alignment, temporal consistency, quality, scale, and editing diversity, but prior datasets largely represent independent clips or operations rather than connected creation workflows.

Text-to-Video Generation Models and Datasets.

  • Diffusion models improved text–video alignment, and later latent-diffusion and spatiotemporal-transformer designs improved visual quality and temporal coherence; recent models support multi-shot generation and audio-visual synchronization.Early GAN-based and autoregressive methods mainly used unconditional or weak conditioning, while early video diffusion models still struggled with temporal consistency.Source: Text-to-Video Generation Models and… · p. 2
  • Large-scale text–video datasets enabled foundational generation, but prior datasets organized samples as independent pairs without connecting generation roots to editing lineage, order, or intermediate states.MSR-VTT and ActivityNet were also widely used for understanding, captioning, and retrieval.Source: Text-to-Video Generation Models and… · p. 2

Video Editing Models and Datasets.

  • Video-editing methods extended instruction-driven image editing with temporal consistency techniques, but mostly addressed individual operations; data preserving successive prompts and videos remained limited.Tune-A-Video and FateZero used tuning or attention mechanisms to preserve source structure, while Token-Flow and CoDeF modeled temporal correspondences.Source: Video Editing Models and Datasets · p. 2
  • Video-editing datasets broadened scale, quality, and diversity, yet generally stored each operation as an independent source–instruction–target triplet without linking it to a generation root or preserving editing order and intermediate states.Recent work explored iterative editing and cross-turn consistency, but successive editing data remained limited.Source: Video Editing Models and Datasets · pp. 2–3

Dataset Characteristics

Sora100K represents AI-Native video creation as structured trajectories rather than isolated clips, preserving multi-shot structure and synchronized audio-visual information.

Dataset Characteristics

  • Sora100K models video creation as connected trajectories and retains multi-shot and audio-visual information beyond isolated clips.Source: Dataset Characteristics · p. 3

Structure-Level Characteristics

  • Its three subsets capture complementary trajectory structures: generation prompts and roots, single-turn editing edges, and complete multi-turn root-to-node paths.A creation tree contains video states derived from an initial prompt; a trajectory is an ordered root-to-node path through that tree.Source: Structure-Level Characteristics · p. 3

Video-Level Characteristics

  • Sora100K preserves audio closely synchronized with visual content, providing data for joint audio-visual generation where earlier datasets often contained visual-only samples.Source: Video-Level Characteristics · p. 4
  • Most collected Sora100K videos have multiple shots and generally maintain character consistency across shot transitions, unlike earlier datasets dominated by single-shot clips.Source: Video-Level Characteristics · p. 4

Dataset Construction and Annotation

Sora100K reconstructs AI-native video creation as connected generation roots, editing edges, and multi-turn trajectories, with hierarchical annotations and quality-checked metadata. Its statistics describe the dataset’s naturally imbalanced composition, while human comparison supports the reliability of its automatic labels.

Data Curation and Quality Filtering

  • From approximately 180K public Sora2 records, staged filtering retained 137K, 111K, then 103.4K; the final dataset contains 18,451 roots, 76,964 editing edges, and 8,024 multi-turn trajectories.Valid initial generations are retained as roots even without edits; each valid source–instruction–target relation forms an edge, and only connected paths with at least three video states form multi-turn trajectories. Representative trajectories showed lineage, edit order, and intermediate states consistent with recovered metadata.Source: Data Curation and Quality Filtering · p. 4

Dataset Annotation

  • Hierarchical annotations characterize generation roots, single-turn editing edges, and multi-turn trajectory structure, using Qwen2.5-VL-7B (Bai et al. 2025) for root semantics and editing operations.Roots receive semantic category, duration, frame count, shot count, and prompt length labels; edges receive multi-label editing categories plus video attributes; trajectories preserve operation category, lineage, and chain length.Source: Data Curation and Quality Filtering · p. 4, Dataset Annotation · p. 5

Dataset Statistics

  • Of 8,024 multi-turn trajectories, 92.2% contain 3–5 video states, and object-level operations are the largest trajectory-level category at 4.38% of the full dataset.Three-, four-, and five-state trajectories make up 61.5%, 18.4%, and 12.3%, respectively; 7.8% exceed five states. Other trajectory-level operation shares are style 1.83%, plot 0.65%, scene 0.50%, and other 0.44%.Source: Dataset Statistics · p. 5, Figure 4 · p. 5
  • Generation roots comprise 17.8% of structured records, while single-turn editing edges are led by object-level operations at 38.68% of the full dataset.People-related generation is 13.68% of the full dataset, followed by animals 2.33%, transportation 0.49%, scenery 0.43%, and other categories 0.86%. Edge shares are style 20.09%, scene 6.70%, narrative and temporal 3.72%, and other 5.21%; most videos are about 10 seconds and 300 frames at 30 FPS, and 65.5% are multi-shot. Prompt-length shares are 52.2% at no more than 10 words, 16.6% at 11–25, 10.3% at 26–50, 10.7% at 51–100, and 10.3% above 100 words.Source: Dataset Statistics · p. 5, Figure 4 · p. 5

Natural Distribution and Annotation Reliability

  • Sora100K preserves natural creation-process imbalances observed on an AI-native video platform, while retaining metadata that enables resampling or balanced subsets.People-related generation and common editing operations account for relatively large proportions rather than being balanced during construction.Source: Natural Distribution and Annotation… · pp. 5–6
  • Human comparison supports the reliability of Qwen2.5-VL-7B labels, with 98.19% accuracy, Cohen’s κ of 0.959, and weighted F1 of 98.26%.The evaluation sampled generation roots and editing edges and compared automatic and human annotations under the same taxonomies.Source: Natural Distribution and Annotation… · p. 6

Experiment

The Sora100K-adapted pipeline outperforms the original over two editing turns, while results across generation, editing, and successive turns show both gains and continued challenges.

Experiment

  • Because open-source models do not support training on complete multimodal trajectories, generation and editing components are adapted separately and recombined for successive-turn evaluation.Each component is also evaluated independently to identify the source of performance gains.Source: Experiment · p. 6

Experiment Setting

  • The iterative evaluation uses LTX-2-T2AV as generator and LTX-2-T2AV-IC as editor, adapting each with LoRA on 3,000 roots or 3,000 edges.The generator creates an initial video from the root prompt; the editor then applies original instructions sequentially, using each output as the next input. Every resulting state is evaluated against the original pipeline.Source: Experiment Setting · p. 6

Experiment Results

  • The Sora100K-adapted pipeline achieves a higher two-turn average score than the original, 62.4 versus 60.6.Both pipelines degrade across turns. Aesthetic quality averages 46.9 versus 41.5, while editing effectiveness averages 43.0 for both; successive editing remains challenging as errors accumulate.Source: Experiment Results · p. 6, Table 2 · p. 6
  • On 200 held-out prompts, Sora100K-adapted generation scores 76.43 overall and leads on five of six metrics.Compared with the original model, aesthetic quality rises from 59.11 to 62.98, imaging quality from 47.84 to 57.22, and ArcFace from 48.30 to 59.70; motion smoothness is nearly unchanged. Qualitative results show cleaner structures and more consistent subjects across shots, supporting a stronger initial state for trajectory construction.Source: Experiment Results · pp. 6–7, Table 3 · p. 7, Figure 6 · p. 7
  • Editing adaptation raises the average score from 56.0 to 59.4, improving instruction following, editing effectiveness, and aesthetic quality.Sinst increases from 43.4 to 47.9, Seff from 38.7 to 44.2, and SAes from 49.8 to 53.8; subject consistency changes slightly from 92.0 to 91.7. Qualitative results show stronger editing and visual quality, indicating improved transitions between successive states.Source: Experiment Results · p. 7, Table 4 · p. 7, Figure 7 · p. 7

Conclusion

Sora100K represents AI-native video creation as curated audio-visual trajectories, preserving editing lineage and video structure. Experiments show effective supervision for generation and single-turn editing, while open-source models remain limited in suitability and trajectory-following capability.

Conclusion

  • Sora100K supervision improves video-generation and single-turn-editing performance in visual quality, cross-shot consistency, editing effectiveness, and instruction following.The conclusion identifies these as areas of enhanced performance.Source: Conclusion · p. 7
  • Sora100K structures text-to-video records as roots, single-turn edits as edges, and multi-turn edits as trajectories, preserving lineage, intermediate states, multi-shot structure, and synchronized audio.Systematic filtering, workflow reconstruction, structured annotation, and human validation produce a curated resource for studying AI-native video creation.Source: Conclusion · p. 7
  • Suitable open-source models remain limited, and most cannot handle creation trajectories; many cited models are derived from LTX-2 or incompatible with the input modalities (Team et al. 2026; Zhang et al. 2026).The passage describes trajectory-capable models as close-source.Source: Conclusion · p. 7

Appendix · Motivation and Dataset Gap

Existing public video-generation and editing datasets are largely clip-level and lack explicit support for multi-shot, audio-visual creation. Sora100K systematically covers long-term content, multi-shot structure, and audio-visual synchronization, supporting research on these capabilities.

Motivation and Dataset Gap

  • Existing public generation and editing datasets primarily contain clip-level, no-audio samples, with limited explicit support for multi-shot structure and audio-visual creation.The paper says these gaps leave the datasets insufficient for studying long-range temporal consistency, cross-shot composition, and audiovisual coherence.Source: Motivation and Dataset Gap · p. 10
  • Figure 8 presents representative text-to-video samples from Panda-70M, Koala-36M, and Sora100K to illustrate differences in shot organization and visual content.The caption identifies the compared datasets and the aspects illustrated; it does not quantify those differences.Source: Figure 8 · p. 10
  • Figure 9 presents representative editing samples from OpenVE-3M and Sora100K, showing source and edited videos with content and style modifications.The caption identifies the sample comparison and modification types without establishing a dataset-wide result.Source: Figure 9 · p. 10
  • Sora100K systematically covers long-term content, multi-shot structures, and audio-visual synchronization.The paper presents this coverage as more aligned with current video-generation trends and real-user creative needs.Source: Motivation and Dataset Gap · p. 10
  • Sora100K’s coverage provides a resource foundation for research on long-range dependency modeling, cross-shot organization, and audio-visual collaborative generation.The paper links this foundation to its coverage of long-term content, multi-shot structure, and audio-visual synchronization.Source: Motivation and Dataset Gap · p. 10
  • Sora100K’s long-term, multi-shot, and synchronized audio-visual attributes are presented as more relevant to real-world scenarios and current creative needs.The paper connects the dataset’s attribute coverage to trends in video generation and the creative needs of real users.Source: Motivation and Dataset Gap · p. 10

Dataset Samples · Text-to-Video Generation Subset

The Text-to-Video Generation subset assigns each video a primary label based on its main narrative theme across eight semantic categories, with representative samples shown in Figures 10–11.

Text-to-Video Generation Subset

Single-Turn Video Editing Subset … Experiment Result.

Sora100K organizes video generation and editing as annotated subsets and ordered editing trajectories. Qualitative comparisons show that the adapted model preserves character appearance, scene structure, and earlier edits across successive turns.

Single-Turn Video Editing Subset / Single-Turn Video Editing Statistics

  • Object-level edits make up 52% of the single-turn subset, style-level edits 27%, temporal edits 9%, scene-level edits 5%, and other operations 7%.The subset analyzes editing categories alongside video duration, frame count, and shot count; object edits include localized changes to visible entities, while style edits change visual appearance.Source: Single-Turn Video Editing Subset · pp. 11–12

Multi-Turn Video Editing Subset / Multi-Turn Video Editing Statistics

Dataset Annotation and Statistics / Annotation Fields

  • Generation records store prompts, supplementary descriptions, video attributes, and semantic categories; single-turn records store source–instruction–target transitions and labels, while multi-turn records organize states into editing chains.The fields include video duration, frame count, scene or shot count, and chain length.Source: Dataset Annotation and Statistics · p. 12, Annotation Fields · p. 13

Overall Dataset Composition

  • Of 103,439 Sora100K entries, 76,964 (74.4%) are single-turn edits, 18,451 (17.8%) are text-to-video generations, and 8,024 (7.8%) are multi-turn edits.Source: Overall Dataset Composition · p. 13

Text-to-Video Generation Statistics

  • Generation prompts of no more than 10 words account for 52.20% of the text-to-video subset, and videos lasting 9.5–10.5 seconds account for 73.48%.People-related prompts account for 76.85% and animal-related prompts for 13.11%; videos with more than 10 scenes form the smallest scene-count group.Source: Text-to-Video Generation Statistics · pp. 13–14

Experiment Setting

  • Text-to-video adaptation used 3,000 generation videos for five epochs, while editing adaptation used 3,000 single-turn samples and also trained for five epochs.Both experiments used LoRA with rank 32 and learning rate 1 × 10−4 on a single NVIDIA H100 GPU; generation videos used 121 frames and editing videos used 81 frames.Source: Experiment Setting · pp. 14–15

Experiment Result

  • Text-to-video comparisons show sampled-frame differences in visual quality, subject preservation, and cross-shot consistency, while single-turn examples cover object removal and merging, lighting effects, and watermark insertion.Source: Figure 14 · p. 16, Figure 15 · p. 17
  • Across four successive editing instructions, the adapted model better preserves character appearance and scene structure while accumulating changes and retaining previous editing effects.A second comparison starts from the same source video and applies a high-motion anime style followed by a halo.Source: Figure 16 · p. 17, Figure 17 · p. 18
Loading 2610.11770v1…