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arxiv: 2512.20340 · v3 · submitted 2025-12-23 · 💻 cs.CV

The devil is in the details: Enhancing Video Virtual Try-On via Keyframe-Driven Details Injection

Qingdong He , Xueqin Chen , Yanjie Pan , Peng Tang , Pengcheng Xu , Zhenye Gan , Chengjie Wang , Xiaobin Hu
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Jiangning Zhang Yabiao Wang
This is my paper

Pith reviewed 2026-05-16 20:03 UTC · model grok-4.3

classification 💻 cs.CV
keywords video virtual try-ondiffusion transformerkeyframe injectiongarment fidelitybackground integrityDiT blocksViT-HD dataset
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The pith

Keyframe-driven details injection into standard DiT blocks improves garment fidelity and background integrity in video virtual try-on.

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

The paper proposes KeyTailor, a framework that samples informative keyframes from input videos using an instruction-guided strategy and then applies two specialized modules to extract garment dynamics and optimize background consistency. These distilled details are injected directly into unmodified diffusion transformer blocks together with pose, mask, and noise latents. The approach targets shortcomings in existing DiT-based video virtual try-on methods that fail to capture fine-grained motion or preserve scene integrity while adding computational overhead. The authors also release ViT-HD, a dataset of 15,070 high-resolution videos, to support more effective training.

Core claim

KeyTailor uses keyframe sampling to identify frames rich in foreground dynamics and background consistency, then routes garment information through a details enhancement module and background information through a collaborative optimization module. The resulting enriched latents are combined with standard conditioning inputs and fed into unmodified DiT blocks, producing try-on videos that maintain realistic garment movement and scene coherence across frames without architectural changes or extra interaction modules.

What carries the argument

Keyframe-driven details injection strategy, which samples keyframes and employs garment details enhancement and collaborative background optimization modules to distill information for direct injection into standard DiT blocks.

Load-bearing premise

Keyframes inherently contain both foreground dynamics and background consistency in sufficient quality to allow the two modules to distill useful details for injection.

What would settle it

An ablation that disables the keyframe sampling and detail-injection modules entirely and measures whether garment fidelity and background integrity scores on the same test videos drop to or below those of the strongest baseline DiT method.

Figures

Figures reproduced from arXiv: 2512.20340 by Chengjie Wang, Jiangning Zhang, Pengcheng Xu, Peng Tang, Qingdong He, Xiaobin Hu, Xueqin Chen, Yabiao Wang, Yanjie Pan, Zhenye Gan.

Figure 1
Figure 1. Figure 1: KeyTailor enables generating realistic and natural try-on videos with fine-grained consistency in both garment and background [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: (a) Comparison of garment details; (b) Comparison of [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Dataset overview. to simple, repetitive runway scenes. Moreover, their limited scale still falls short of the growing need for large, high￾quality video data. To address these limitations, we curate a new dataset, ViT-HD, which significantly expands both the scale and quality of available resources [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Overall framework of KeyTailor. KeyTailor takes as input a reference garment image Iref, a source video Vin, its correspond￾ing agnostic video Vagn, agnostic masks Magn, and pose representations P. These inputs are encoded into garment-related latents Lg, background-related latents Lbg, pose latents Lp, and resized masks Lm. Specifically, garment-related latents are generated by the GDDE module, background… view at source ↗
Figure 5
Figure 5. Figure 5: Qualitative comparison of video virtual try-on results on the ViViD dataset (1st column), our ViT-HD dataset (2nd column), and [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Qualitative results and comparisons in person-to-video garment transfer scenarios. Our method combines background, person, [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: User Study. We report pairwise preference rates from the [PITH_FULL_IMAGE:figures/full_fig_p008_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Qualitative comparison of of KeyTailor with variants on [PITH_FULL_IMAGE:figures/full_fig_p008_8.png] view at source ↗
read the original abstract

Although diffusion transformer (DiT)-based video virtual try-on (VVT) has made significant progress in synthesizing realistic videos, existing methods still struggle to capture fine-grained garment dynamics and preserve background integrity across video frames. They also incur high computational costs due to additional interaction modules introduced into DiTs, while the limited scale and quality of existing public datasets also restrict model generalization and effective training. To address these challenges, we propose a novel framework, KeyTailor, along with a large-scale, high-definition dataset, ViT-HD. The core idea of KeyTailor is a keyframe-driven details injection strategy, motivated by the fact that keyframes inherently contain both foreground dynamics and background consistency. Specifically, KeyTailor adopts an instruction-guided keyframe sampling strategy to filter informative frames from the input video. Subsequently,two tailored keyframe-driven modules, the garment details enhancement module and the collaborative background optimization module, are employed to distill garment dynamics into garment-related latents and to optimize the integrity of background latents, both guided by keyframes.These enriched details are then injected into standard DiT blocks together with pose, mask, and noise latents, enabling efficient and realistic try-on video synthesis. This design ensures consistency without explicitly modifying the DiT architecture, while simultaneously avoiding additional complexity. In addition, our dataset ViT-HD comprises 15, 070 high-quality video samples at a resolution of 810*1080, covering diverse garments. Extensive experiments demonstrate that KeyTailor outperforms state-of-the-art baselines in terms of garment fidelity and background integrity across both dynamic and static scenarios.

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

3 major / 3 minor

Summary. The paper proposes KeyTailor, a framework for video virtual try-on that uses an instruction-guided keyframe sampling strategy to select informative frames, followed by a garment details enhancement module and a collaborative background optimization module to distill and inject enriched latents into unmodified DiT blocks along with pose, mask, and noise. It also introduces the ViT-HD dataset of 15,070 high-resolution videos. The central claim is that this keyframe-driven approach achieves superior garment fidelity and background integrity compared to state-of-the-art baselines in both dynamic and static scenarios while avoiding additional DiT complexity.

Significance. If the empirical claims hold, the work offers a practical efficiency gain for DiT-based VVT by leveraging inherent keyframe properties rather than architectural modifications, and the release of ViT-HD addresses a clear data-scale limitation in the field. The design credits the avoidance of extra interaction modules and the focus on detail injection as strengths for reproducibility and generalization.

major comments (3)
  1. [§3.2] §3.2 (Keyframe-driven modules): The core assumption that keyframes inherently supply both foreground garment dynamics and background consistency for effective distillation is load-bearing for the performance claim, yet the manuscript provides no targeted validation (e.g., ablation on motion speed or occlusion cases) showing that the distilled latents actually capture critical folds or rapid changes; without this, gains in fidelity do not necessarily follow from the architecture.
  2. [§4] §4 (Experiments): The outperformance claim over baselines is central but the reported results lack quantitative metrics (e.g., FID, LPIPS, or garment-specific scores), ablation tables, error analysis, or statistical tests; the abstract's qualitative statement alone is insufficient to verify the central performance assertion.
  3. [§3.3] §3.3 (Injection mechanism): The description of how garment-related and background-optimized latents are combined with pose/mask/noise inputs and injected into standard DiT blocks is high-level; precise equations or a diagram showing the latent fusion operation are needed to confirm that no implicit modifications to the DiT occur and to support reproducibility.
minor comments (3)
  1. [Abstract] Abstract: Typo in 'Subsequently,two' (missing space after comma).
  2. [Abstract] Abstract: Dataset size written as '15, 070' should be standardized to '15,070'.
  3. [§4.1] §4.1: Figure captions for dynamic vs. static examples could explicitly note the keyframe selection criteria used in each case.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment point by point below and commit to revising the manuscript accordingly to strengthen the presentation and validation of our claims.

read point-by-point responses

  1. Referee: [§3.2] §3.2 (Keyframe-driven modules): The core assumption that keyframes inherently supply both foreground garment dynamics and background consistency for effective distillation is load-bearing for the performance claim, yet the manuscript provides no targeted validation (e.g., ablation on motion speed or occlusion cases) showing that the distilled latents actually capture critical folds or rapid changes; without this, gains in fidelity do not necessarily follow from the architecture.

    Authors: We agree that targeted validation of the keyframe assumption is important to support the performance claims. The manuscript motivates the approach by noting that keyframes inherently contain foreground dynamics and background consistency, but we acknowledge the lack of specific ablations on motion speed or occlusion. In the revision, we will add experiments including ablations on these cases and analysis showing how the distilled latents capture critical folds and rapid changes. revision: yes

  2. Referee: [§4] §4 (Experiments): The outperformance claim over baselines is central but the reported results lack quantitative metrics (e.g., FID, LPIPS, or garment-specific scores), ablation tables, error analysis, or statistical tests; the abstract's qualitative statement alone is insufficient to verify the central performance assertion.

    Authors: We acknowledge that the experimental results would be more convincing with additional quantitative support. While the manuscript includes qualitative comparisons demonstrating superiority in garment fidelity and background integrity, we will expand the experiments section in the revision to include quantitative metrics such as FID, LPIPS, garment-specific scores, full ablation tables, error analysis, and statistical tests. revision: yes

  3. Referee: [§3.3] §3.3 (Injection mechanism): The description of how garment-related and background-optimized latents are combined with pose/mask/noise inputs and injected into standard DiT blocks is high-level; precise equations or a diagram showing the latent fusion operation are needed to confirm that no implicit modifications to the DiT occur and to support reproducibility.

    Authors: We agree that the injection mechanism description can be made more precise to aid reproducibility. The manuscript emphasizes that the design injects enriched latents into unmodified DiT blocks without additional complexity, but we will revise §3.3 to include precise equations for the latent fusion operation and a diagram illustrating the combination with pose, mask, and noise inputs. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper introduces a new framework (KeyTailor) and dataset (ViT-HD) with keyframe-driven modules for details injection into unmodified DiT blocks. No equations, derivations, or fitted parameters are described that reduce any prediction or result to its own inputs by construction. The motivating assumption about keyframes is presented as an empirical observation rather than a self-referential definition or self-citation chain. The central claims rest on architectural novelty and experimental validation, remaining self-contained without circular reduction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The framework rests on the domain assumption that keyframes capture the necessary dynamics and consistency, plus the implicit assumption that the new modules can be injected into unmodified DiT blocks without performance loss. No free parameters or invented physical entities are described.

axioms (1)
  • domain assumption Keyframes inherently contain both foreground dynamics and background consistency
    Explicitly stated as the motivation for the keyframe-driven strategy in the abstract.

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discussion (0)

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