# ShineOn: Illuminating Design Choices for Practical Video-based Virtual Clothing Try-on

arXiv: https://arxiv.org/abs/2012.10495 · HTML: https://mlsanity.com/paper/2012.10495/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2020.

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

- [ViViD: Video Virtual Try-on using Diffusion Models](https://mlsanity.com/paper/2405.11794/) (2024)
- [Tunnel Try-on: Excavating Spatial-temporal Tunnels for High-quality Virtual Try-on in Videos](https://mlsanity.com/paper/2404.17571/) (2024)
- [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)
- [Per Garment Capture and Synthesis for Real-time Virtual Try-on](https://mlsanity.com/paper/2109.04654/) (2021)
- [DM-VTON: Distilled Mobile Real-time Virtual Try-On](https://mlsanity.com/paper/2308.13798/) (2023)
- [Smart Fashion: A Review of AI Applications in the Fashion & Apparel Industry](https://mlsanity.com/paper/2111.00905/) (2021)
- [Dress&Dance: Dress up and Dance as You Like It - Technical Preview](https://mlsanity.com/paper/2508.21070/) (2025)
- [fAshIon after fashion: A Report of AI in Fashion](https://mlsanity.com/paper/2105.03050/) (2021)
- [FashionFlow: Leveraging Diffusion Models for Dynamic Fashion Video Synthesis from Static Imagery](https://mlsanity.com/paper/2310.00106/) (2023)
- [ChronoTailor: Harnessing Attention Guidance for Fine-Grained Video Virtual Try-On](https://mlsanity.com/paper/2506.05858/) (2025)
- [Fashionability-Enhancing Outfit Image Editing with Conditional Diffusion Models](https://mlsanity.com/paper/2412.18421/) (2024)
- [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)

Virtual try-on has garnered interest as a neural rendering benchmark task to evaluate complex object transfer and scene composition. Recent works in virtual clothing try-on feature a plethora of possible architectural and data representation choices. However, they present little clarity on quantifying the isolated visual effect of each choice, nor do they specify the hyperparameter details that are key to experimental reproduction. Our work, ShineOn, approaches the try-on task from a bottom-up approach and aims to shine light on the visual and quantitative effects of each experiment. We build a series of scientific experiments to isolate effective design choices in video synthesis for virtual clothing try-on. Specifically, we investigate the effect of different pose annotations, self-attention layer placement, and activation functions on the quantitative and qualitative performance of video virtual try-on. We find that DensePose annotations not only enhance face details but also decrease memory usage and training time. Next, we find that attention layers improve face and neck quality. Finally, we show that GELU and ReLU activation functions are the most effective in our experiments despite the appeal of newer activations such as Swish and Sine. We will release a well-organized code base, hyperparameters, and model checkpoints to support the reproducibility of our results. We expect our extensive experiments and code to greatly inform future design choices in video virtual try-on. Our code may be accessed at https://github.com/andrewjong/ShineOn-Virtual-Tryon.