# Tunnel Try-on: Excavating Spatial-temporal Tunnels for High-quality Virtual Try-on in Videos

arXiv: https://arxiv.org/abs/2404.17571 · HTML: https://mlsanity.com/paper/2404.17571/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 43 of its 52 references that are in the map.
- First posted on arXiv 26 Apr 2024.

## Builds on (most cited first, 30 of 43)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning](https://mlsanity.com/paper/2307.04725/) (2023)
- [Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation](https://mlsanity.com/paper/2311.17117/) (2023)
- [Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?](https://mlsanity.com/paper/1711.09577/) (2017)
- [Paint by Example: Exemplar-based Image Editing with Diffusion Models](https://mlsanity.com/paper/2211.13227/) (2022)
- [AnyDoor: Zero-shot Object-level Image Customization](https://mlsanity.com/paper/2307.09481/) (2023)
- [Video-to-Video Synthesis](https://mlsanity.com/paper/1808.06601/) (2018)
- [MagicAnimate: Temporally Consistent Human Image Animation using Diffusion Model](https://mlsanity.com/paper/2311.16498/) (2023)
- [VITON-HD: High-Resolution Virtual Try-On via Misalignment-Aware Normalization](https://mlsanity.com/paper/2103.16874/) (2021)
- [EDGE: Editable Dance Generation From Music](https://mlsanity.com/paper/2211.10658/) (2022)
- [VITON: An Image-based Virtual Try-on Network](https://mlsanity.com/paper/1711.08447/) (2017)
- [Thin-Plate Spline Motion Model for Image Animation](https://mlsanity.com/paper/2203.14367/) (2022)
- [Toward Characteristic-Preserving Image-based Virtual Try-On Network](https://mlsanity.com/paper/1807.07688/) (2018)
- [Conditional Image-to-Video Generation with Latent Flow Diffusion Models](https://mlsanity.com/paper/2303.13744/) (2023)
- [StableVITON: Learning Semantic Correspondence with Latent Diffusion Model for Virtual Try-On](https://mlsanity.com/paper/2312.01725/) (2023)
- [DreamPose: Fashion Image-to-Video Synthesis via Stable Diffusion](https://mlsanity.com/paper/2304.06025/) (2023)
- [Dress Code: High-Resolution Multi-Category Virtual Try-On](https://mlsanity.com/paper/2204.08532/) (2022)
- [LaDI-VTON: Latent Diffusion Textual-Inversion Enhanced Virtual Try-On](https://mlsanity.com/paper/2305.13501/) (2023)
- [Parser-Free Virtual Try-on via Distilling Appearance Flows](https://mlsanity.com/paper/2103.04559/) (2021)
- [DisCo: Disentangled Control for Realistic Human Dance Generation](https://mlsanity.com/paper/2307.00040/) (2023)
- [TryOnDiffusion: A Tale of Two UNets](https://mlsanity.com/paper/2306.08276/) (2023)

## Built on by (most cited first, 30 of 32)

- [Zero-shot Image Editing with Reference Imitation](https://mlsanity.com/paper/2406.07547/) (2024)
- [ViViD: Video Virtual Try-on using Diffusion Models](https://mlsanity.com/paper/2405.11794/) (2024)
- [CatV2TON: Taming Diffusion Transformers for Vision-Based Virtual Try-On with Temporal Concatenation](https://mlsanity.com/paper/2501.11325/) (2025)
- [Human Motion Video Generation: A Survey](https://mlsanity.com/paper/2509.03883/) (2025)
- [OmniV2V: Versatile Video Generation and Editing via Dynamic Content Manipulation](https://mlsanity.com/paper/2506.01801/) (2025)
- [DreamDance: Animating Human Images by Enriching 3D Geometry Cues from 2D Poses](https://mlsanity.com/paper/2412.00397/) (2024)
- [DreamVVT: Mastering Realistic Video Virtual Try-On in the Wild via a Stage-Wise Diffusion Transformer Framework](https://mlsanity.com/paper/2508.02807/) (2025)
- [MagicTryOn: Harnessing Diffusion Transformer for Garment-Preserving Video Virtual Try-on](https://mlsanity.com/paper/2505.21325/) (2025)
- [VITON-DiT: Learning In-the-Wild Video Try-On from Human Dance Videos via Diffusion Transformers](https://mlsanity.com/paper/2405.18326/) (2024)
- [Pursuing Temporal-Consistent Video Virtual Try-On via Dynamic Pose Interaction](https://mlsanity.com/paper/2505.16980/) (2025)
- [Dynamic Try-On: Taming Video Virtual Try-on with Dynamic Attention Mechanism](https://mlsanity.com/paper/2412.09822/) (2024)
- [HelloMeme: Integrating Spatial Knitting Attentions to Embed High-Level and Fidelity-Rich Conditions in Diffusion Models](https://mlsanity.com/paper/2410.22901/) (2024)
- [IGR: Improving Diffusion Model for Garment Restoration from Person Image](https://mlsanity.com/paper/2412.11513/) (2024)
- [iTryOn: Mastering Interactive Video Virtual Try-On with Spatial-Semantic Guidance](https://mlsanity.com/paper/2605.21431/) (2026)
- [3DV-TON: Textured 3D-Guided Consistent Video Try-on via Diffusion Models](https://mlsanity.com/paper/2504.17414/) (2025)
- [Video Virtual Try-on with Conditional Diffusion Transformer Inpainter](https://mlsanity.com/paper/2506.21270/) (2025)
- [Once Is Enough: Lightweight DiT-Based Video Virtual Try-On via One-Time Garment Appearance Injection](https://mlsanity.com/paper/2510.07654/) (2025)
- [TryOnCrafter: Unleashing Camera Trajectories for Realistic Video Virtual Try-on via a Renderable 4D Try-on Proxy](https://mlsanity.com/paper/2606.26092/) (2026)
- [Dress&Dance: Dress up and Dance as You Like It - Technical Preview](https://mlsanity.com/paper/2508.21070/) (2025)
- [From Corpora to Co-Evolving Capabilities: Capability-Centric Data Design for Generalist Image Generation](https://mlsanity.com/paper/2608.18076/) (2026)
- [Masked Extended Attention for Zero-Shot Virtual Try-On In The Wild](https://mlsanity.com/paper/2406.15331/) (2024)
- [ChronoTailor: Harnessing Attention Guidance for Fine-Grained Video Virtual Try-On](https://mlsanity.com/paper/2506.05858/) (2025)
- [Low-Barrier Dataset Collection with Real Human Body for Interactive Per-Garment Virtual Try-On](https://mlsanity.com/paper/2506.10468/) (2025)
- [Real-Time Per-Garment Virtual Try-On with Temporal Consistency for Loose-Fitting Garments](https://mlsanity.com/paper/2506.12348/) (2025)
- [RoomEditor++: A Parameter-Sharing Diffusion Architecture for High-Fidelity Furniture Synthesis](https://mlsanity.com/paper/2512.17573/) (2025)
- [Oxygen-TryOn: Fashion-Native Foundation Model for Any-item Virtual Try-On](https://mlsanity.com/paper/2607.21694/) (2026)
- [SwiftTry: Fast and Consistent Video Virtual Try-On with Diffusion Models](https://mlsanity.com/paper/2412.10178/) (2024)
- [TalkFashion: Intelligent Virtual Try-On Assistant Based on Multimodal Large Language Model](https://mlsanity.com/paper/2507.05790/) (2025)
- [DreamSwapV: Mask-guided Subject Swapping for Any Customized Video Editing](https://mlsanity.com/paper/2508.14465/) (2025)
- [Virtual Fitting Room: Generating Arbitrarily Long Videos of Virtual Try-On from a Single Image -- Technical Preview](https://mlsanity.com/paper/2509.04450/) (2025)

## Abstract (arXiv)

Video try-on is a challenging task and has not been well tackled in previous works. The main obstacle lies in preserving the details of the clothing and modeling the coherent motions simultaneously. Faced with those difficulties, we address video try-on by proposing a diffusion-based framework named "Tunnel Try-on." The core idea is excavating a "focus tunnel" in the input video that gives close-up shots around the clothing regions. We zoom in on the region in the tunnel to better preserve the fine details of the clothing. To generate coherent motions, we first leverage the Kalman filter to construct smooth crops in the focus tunnel and inject the position embedding of the tunnel into attention layers to improve the continuity of the generated videos. In addition, we develop an environment encoder to extract the context information outside the tunnels as supplementary cues. Equipped with these techniques, Tunnel Try-on keeps the fine details of the clothing and synthesizes stable and smooth videos. Demonstrating significant advancements, Tunnel Try-on could be regarded as the first attempt toward the commercial-level application of virtual try-on in videos.