# D4-VTON: Dynamic Semantics Disentangling for Differential Diffusion based Virtual Try-On

arXiv: https://arxiv.org/abs/2407.15111 · HTML: https://mlsanity.com/paper/2407.15111/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 44 references that are in the map.
- First posted on arXiv 21 Jul 2024.

## Builds on (most cited first, 30 of 35)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [Demystifying MMD GANs](https://mlsanity.com/paper/1801.01401/) (2018)
- [The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables](https://mlsanity.com/paper/1611.00712/) (2016)
- [Paint by Example: Exemplar-based Image Editing with Diffusion Models](https://mlsanity.com/paper/2211.13227/) (2022)
- [VITON-HD: High-Resolution Virtual Try-On via Misalignment-Aware Normalization](https://mlsanity.com/paper/2103.16874/) (2021)
- [VITON: An Image-based Virtual Try-on Network](https://mlsanity.com/paper/1711.08447/) (2017)
- [Toward Characteristic-Preserving Image-based Virtual Try-On Network](https://mlsanity.com/paper/1807.07688/) (2018)
- [View Synthesis by Appearance Flow](https://mlsanity.com/paper/1605.03557/) (2016)
- [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)
- [Towards Photo-Realistic Virtual Try-On by Adaptively GeneratingleftrightarrowPreserving Image Content](https://mlsanity.com/paper/2003.05863/) (2020)
- [Parser-Free Virtual Try-on via Distilling Appearance Flows](https://mlsanity.com/paper/2103.04559/) (2021)
- [High-Resolution Virtual Try-On with Misalignment and Occlusion-Handled Conditions](https://mlsanity.com/paper/2206.14180/) (2022)
- [Taming the Power of Diffusion Models for High-Quality Virtual Try-On with Appearance Flow](https://mlsanity.com/paper/2308.06101/) (2023)
- [GP-VTON: Towards General Purpose Virtual Try-on via Collaborative Local-Flow Global-Parsing Learning](https://mlsanity.com/paper/2303.13756/) (2023)
- [Style-Based Global Appearance Flow for Virtual Try-On](https://mlsanity.com/paper/2204.01046/) (2022)
- [Single Stage Virtual Try-on via Deformable Attention Flows](https://mlsanity.com/paper/2207.09161/) (2022)
- [Contrastive Grouping with Transformer for Referring Image Segmentation](https://mlsanity.com/paper/2309.01017/) (2023)
- [Learning Dynamic Alignment via Meta-filter for Few-shot Learning](https://mlsanity.com/paper/2103.13582/) (2021)
- [Towards Scalable Unpaired Virtual Try-On via Patch-Routed Spatially-Adaptive GAN](https://mlsanity.com/paper/2111.10544/) (2021)
- [C-VTON: Context-Driven Image-Based Virtual Try-On Network](https://mlsanity.com/paper/2212.04437/) (2022)
- [From Continuity to Editability: Inverting GANs with Consecutive Images](https://mlsanity.com/paper/2107.13812/) (2021)

## Built on by (most cited first, 14 of 14)

- [Learning Flow Fields in Attention for Controllable Person Image Generation](https://mlsanity.com/paper/2412.08486/) (2024)
- [OmniVTON: Training-Free Universal Virtual Try-On](https://mlsanity.com/paper/2507.15037/) (2025)
- [JCo-MVTON: Jointly Controllable Multi-Modal Diffusion Transformer for Mask-Free Virtual Try-on](https://mlsanity.com/paper/2508.17614/) (2025)
- [TripVVT: A Large-Scale Triplet Dataset and a Coarse-Mask Baseline for In-the-Wild Video Virtual Try-On](https://mlsanity.com/paper/2604.27958/) (2026)
- [Dress&Dance: Dress up and Dance as You Like It - Technical Preview](https://mlsanity.com/paper/2508.21070/) (2025)
- [OrthoTryOn: Geometric Orthogonalization for Conflict-Free Unified Fashion Generation](https://mlsanity.com/paper/2606.27880/) (2026)
- [Learning Implicit Features with Flow Infused Attention for Realistic Virtual Try-On](https://mlsanity.com/paper/2412.11435/) (2024)
- [AvatarVTON: 4D Virtual Try-On for Animatable Avatars](https://mlsanity.com/paper/2510.04822/) (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)
- [Rethinking Garment Conditioning in Diffusion-based Virtual Try-On: Decouple, Don't Denoise](https://mlsanity.com/paper/2511.18775/) (2025)
- [OmniVTON++: Training-Free Universal Virtual Try-On with Principal Pose Guidance](https://mlsanity.com/paper/2602.14552/) (2026)
- [MOFA-VTON: More Fashion Possibilities with Fine-Grained Adaptations in Virtual Try-On](https://mlsanity.com/paper/2606.11148/) (2026)
- [FitVTON: Fit-aware Virtual Try-On via Body-Garment Size Control](https://mlsanity.com/paper/2606.12012/) (2026)
- [SynVAR: Synergizing Spatial and Semantic Alignment in Visual Autoregressive Model](https://mlsanity.com/paper/2608.07948/) (2026)

## Abstract (arXiv)

In this paper, we introduce D4-VTON, an innovative solution for image-based virtual try-on. We address challenges from previous studies, such as semantic inconsistencies before and after garment warping, and reliance on static, annotation-driven clothing parsers. Additionally, we tackle the complexities in diffusion-based VTON models when handling simultaneous tasks like inpainting and denoising. Our approach utilizes two key technologies: Firstly, Dynamic Semantics Disentangling Modules (DSDMs) extract abstract semantic information from garments to create distinct local flows, improving precise garment warping in a self-discovered manner. Secondly, by integrating a Differential Information Tracking Path (DITP), we establish a novel diffusion-based VTON paradigm. This path captures differential information between incomplete try-on inputs and their complete versions, enabling the network to handle multiple degradations independently, thereby minimizing learning ambiguities and achieving realistic results with minimal overhead. Extensive experiments demonstrate that D4-VTON significantly outperforms existing methods in both quantitative metrics and qualitative evaluations, demonstrating its capability in generating realistic images and ensuring semantic consistency.