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arxiv: 2606.11783 · v1 · pith:N47DLBFTnew · submitted 2026-06-10 · 💻 cs.CV

A Comprehensive Ecosystem for Open-Domain Customized Video Generation

Jingxu Zhang , Yuqian Hong , Daneul Kim , Kai Qiu , Qi Dai , Jianmin Bao , Yifan Yang , Xiaoyan Sun
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Chong Luo
This is my paper

Pith reviewed 2026-06-27 10:03 UTC · model grok-4.3

classification 💻 cs.CV
keywords video generationcustomized generationdiffusion transformeridentity preservationlarge-scale datasetparameter-efficient adaptationbenchmark construction
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The pith

CustoMDiT adapts a pretrained diffusion transformer for customized video generation using only 8 percent extra parameters on a new million-scale dataset.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper releases PexelsCustom-1M, a dataset of one million curated identity-text-video triplets spanning more than eight thousand categories, to overcome the shortage of data for identity-preserving video generation. It then presents CustoMDiT, which fine-tunes a multimodal Diffusion Transformer by training only eight percent additional parameters while keeping the rest frozen. Experiments show this setup exceeds earlier methods in both identity fidelity and visual quality. The authors also assemble OpenCustom, a benchmark with over one thousand categories, to evaluate performance on a scale closer to real applications than existing small test sets.

Core claim

Releasing PexelsCustom-1M together with the parameter-efficient CustoMDiT framework allows a pretrained multimodal Diffusion Transformer to become a customized video generator that preserves specific identities across open-domain prompts while adding only eight percent learnable parameters, and this combination outperforms prior state-of-the-art approaches.

What carries the argument

CustoMDiT, the parameter-efficient adaptation framework that inserts a small set of learnable parameters into a pretrained multimodal Diffusion Transformer to steer it toward identity-specific video output.

If this is right

  • Video customization becomes feasible for thousands of identities instead of the roughly one hundred covered by older benchmarks.
  • Only a small fraction of model weights must be updated, lowering the compute needed for each new identity.
  • The OpenCustom benchmark supplies a more demanding and diverse test than prior small-scale sets.
  • Public release of the full dataset, benchmark, and code creates a shared starting point for further work on identity-preserving generation.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same light adaptation approach might transfer to other modalities such as audio or 3D content without full retraining.
  • Curated large datasets of this kind could reduce reliance on massive pretraining runs by supplying better-aligned examples from the start.
  • Combining the framework with explicit motion or temporal modules might further improve long-video coherence.

Load-bearing premise

The one million triplets in PexelsCustom-1M capture real identity attributes across thousands of categories without major curation biases or labeling mistakes that would distort training or evaluation.

What would settle it

If retraining CustoMDiT on PexelsCustom-1M produces videos that fail to match target identities on the OpenCustom benchmark at rates no better than earlier methods, the central performance claim would not hold.

read the original abstract

Recent progress in video generation has shown impressive visual synthesis capabilities. However, open-domain customized video generation remains limited by the lack of large-scale, annotated datasets capturing diverse identity-specific attributes. To address this, we introduce PexelsCustom-1M, the first publicly available million-scale dataset for identity-preserving video generation, containing one million curated <identity, text, video> triplets across 8,000+ categories. Leveraging this, we propose CustoMDiT, a parameter-efficient framework that adapts a pretrained multimodal Diffusion Transformer into a customized video generator with only 8% additional learnable parameters. Our method surpasses prior state-of-the-art. However, benchmarks such as DreamBooth cover only 100 classes, which is insufficient for real-world applications. To overcome this, we construct OpenCustom, a new benchmark with 1,000+ categories, created via cross-dataset knowledge fusion from ImageNet and MS-COCO. Extensive experiments confirm the advantages of both our dataset and model. We will open-source the entire ecosystem--including dataset, pipeline, benchmark, and implementations--to support further research.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper introduces PexelsCustom-1M, the first publicly available million-scale dataset containing one million curated <identity, text, video> triplets across 8,000+ categories for identity-preserving video generation. It proposes CustoMDiT, a parameter-efficient framework adapting a pretrained multimodal Diffusion Transformer into a customized video generator using only 8% additional learnable parameters. The work also constructs the OpenCustom benchmark with 1,000+ categories via cross-dataset fusion from ImageNet and MS-COCO, claims that the method surpasses prior state-of-the-art, and states that extensive experiments confirm the advantages of both the dataset and model, with plans to open-source the full ecosystem.

Significance. If the reported superiority holds under rigorous validation, the work would be significant for enabling open-domain customized video generation by addressing the lack of large-scale annotated datasets and providing an efficient adaptation approach that avoids full retraining. The commitment to open-sourcing the dataset, pipeline, benchmark, and implementations is a clear strength that would support reproducibility and further research in the field.

major comments (2)
  1. [Abstract / Dataset Description] Abstract / Dataset section: The central claim that PexelsCustom-1M 'accurately capture[s] diverse identity-specific attributes' and enables SOTA performance rests on the dataset being free of substantial curation biases or annotation errors, yet the manuscript supplies no validation statistics, inter-annotator agreement scores, diversity metrics (e.g., motion variety or category balance across 8,000+ categories), or error analysis for the one million triplets. This is load-bearing for both the fine-tuning of CustoMDiT and the reliability of the OpenCustom benchmark.
  2. [Abstract / Experiments] Abstract / Experiments section: The assertions that 'our method surpasses prior state-of-the-art' and that 'extensive experiments confirm the advantages' are made without reference to specific quantitative metrics, baselines (e.g., DreamBooth comparisons), ablation results on the 8% parameter adaptation, or error analysis. If these details are missing or insufficiently reported in the experimental sections, the superiority claim cannot be substantiated.
minor comments (1)
  1. [Abstract] Abstract: The phrasing 'Our method surpasses prior state-of-the-art.' is imprecise; it should specify the exact prior methods, metrics (e.g., identity preservation scores, video quality), and benchmark settings for clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on dataset validation and experimental reporting. We address each major comment below and will revise the manuscript to strengthen these aspects.

read point-by-point responses

  1. Referee: [Abstract / Dataset Description] Abstract / Dataset section: The central claim that PexelsCustom-1M 'accurately capture[s] diverse identity-specific attributes' and enables SOTA performance rests on the dataset being free of substantial curation biases or annotation errors, yet the manuscript supplies no validation statistics, inter-annotator agreement scores, diversity metrics (e.g., motion variety or category balance across 8,000+ categories), or error analysis for the one million triplets. This is load-bearing for both the fine-tuning of CustoMDiT and the reliability of the OpenCustom benchmark.

    Authors: We agree that explicit validation statistics are needed to support the dataset claims. The current manuscript describes the curation process but does not report inter-annotator agreement, diversity metrics, or error analysis. In revision we will add a new subsection with these details, including inter-annotator agreement computed on a held-out sample, category balance and motion variety statistics across the 8,000+ categories, and a quantitative error analysis on a random subset of triplets. revision: yes

  2. Referee: [Abstract / Experiments] Abstract / Experiments section: The assertions that 'our method surpasses prior state-of-the-art' and that 'extensive experiments confirm the advantages' are made without reference to specific quantitative metrics, baselines (e.g., DreamBooth comparisons), ablation results on the 8% parameter adaptation, or error analysis. If these details are missing or insufficiently reported in the experimental sections, the superiority claim cannot be substantiated.

    Authors: The experimental section contains quantitative comparisons and ablations, but the abstract and high-level claims do not explicitly reference the metrics or tie them to the 8% adaptation results. We will revise the abstract to cite specific metrics (e.g., FID, CLIP similarity) and ensure the experiments section clearly tabulates DreamBooth baselines, the parameter-efficiency ablation, and error analysis. This will make the superiority statements directly traceable to the reported numbers. revision: partial

Circularity Check

0 steps flagged

No circularity; claims rest on new dataset, benchmark, and empirical adaptation.

full rationale

The paper's central contributions are the creation of PexelsCustom-1M (1M triplets across 8000+ categories), the OpenCustom benchmark (via cross-dataset fusion), and CustoMDiT (8% parameter adaptation of a pretrained DiT). No equations, first-principles derivations, or 'predictions' are described that reduce by construction to fitted inputs or self-citations. Superiority claims are supported by experiments on the new resources rather than self-referential fitting. This matches the reader's assessment of no load-bearing circularity in the derivation chain.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claims rest on the unverified quality of the newly curated dataset and the effectiveness of parameter-efficient adaptation of diffusion transformers; no free parameters are explicitly fitted in the abstract, and no new physical entities are postulated.

axioms (1)
  • domain assumption A pretrained multimodal Diffusion Transformer can be adapted for identity-preserving customized video generation using a small fraction of additional parameters.
    This premise underpins the CustoMDiT framework described in the abstract.

pith-pipeline@v0.9.1-grok · 5742 in / 1194 out tokens · 27793 ms · 2026-06-27T10:03:51.997002+00:00 · methodology

discussion (0)

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Reference graph

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    INTRODUCTION The rapid advancement of video generation has intensified demands for customizable content creation in domains such as advertising and digital media. Customized Video Generation (CVG) seeks to preserve visual identities while embedding them into diverse sce- narios guided by text. Although prior works in customized image [1, 2, 3, 4, 5, 6, 7,...

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    OPEN-DOMAIN DA TA CURA TION 2.1. Data Pre-Processing Pexels-400K contains high-quality videos, each accompanied by a descriptive caption. However, these captions primarily focus on the main subject and its motion, while lacking descriptions of other present identities. To address this limitation, we employ a vision- language model (VLM) [22] to generate s...

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