# RealVVT: Towards Photorealistic Video Virtual Try-on via Spatio-Temporal Consistency

arXiv: https://arxiv.org/abs/2501.08682 · HTML: https://mlsanity.com/paper/2501.08682/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jan 2025.

## Built on by (most cited first, 8 of 8)

- [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)
- [Eevee: Towards Close-up High-resolution Video-based Virtual Try-on](https://mlsanity.com/paper/2511.18957/) (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)
- [OmniTryOn: Video Try-On Anything at Once!](https://mlsanity.com/paper/2606.08514/) (2026)
- [From Mannequin to Human: A Pose-Aware and Identity-Preserving Video Generation Framework for Lifelike Clothing Display](https://mlsanity.com/paper/2510.16833/) (2025)
- [FlowVVTON: Flow-Guided Mask-Free Video Virtual Try-On](https://mlsanity.com/paper/2608.30450/) (2026)
- [TexTailor: Texture-Preserving Video Virtual Try-On via Adaptive Garment Conditioning](https://mlsanity.com/paper/2609.39335/) (2026)

## Abstract (arXiv)

Virtual try-on has emerged as a pivotal task at the intersection of computer vision and fashion, aimed at digitally simulating how clothing items fit on the human body. Despite notable progress in single-image virtual try-on (VTO), current methodologies often struggle to preserve a consistent and authentic appearance of clothing across extended video sequences. This challenge arises from the complexities of capturing dynamic human pose and maintaining target clothing characteristics. We leverage pre-existing video foundation models to introduce RealVVT, a photoRealistic Video Virtual Try-on framework tailored to bolster stability and realism within dynamic video contexts. Our methodology encompasses a Clothing & Temporal Consistency strategy, an Agnostic-guided Attention Focus Loss mechanism to ensure spatial consistency, and a Pose-guided Long Video VTO technique adept at handling extended video sequences.Extensive experiments across various datasets confirms that our approach outperforms existing state-of-the-art models in both single-image and video VTO tasks, offering a viable solution for practical applications within the realms of fashion e-commerce and virtual fitting environments.