InstructVVT: Instruction-Driven Video Virtual Try-On without Auxiliary Spatial Priors

Dingbao Shao; Song Wu; Xinyu Chen; Qian Wang; Jiahang Li; Kuai Jiang; Jiang Lin; Yuhang Liu; Ziyu Chen; Duo Li; Jiaxin Hu; Shengrong Gu; Ziheng Tang; Rongrong Liu; Yanlun Peng; Liang Li; Junlan Feng; Lujia Jin; Ting Zhang; Jian Yang; Zili Yi

arXiv · 2608.14070

InstructVVT: Instruction-Driven Video Virtual Try-On without Auxiliary Spatial Priors

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Abstract

Video virtual try-on is a highly constrained editing task requiring the precise replacement of a target person's clothing while strictly preserving the original video's spatial structure and temporal dynamics. Existing methods heavily rely on auxiliary handcrafted spatial priors (e.g., masks, poses) for editing control. However, these priors are prone to failure in unconstrained real-world videos and often compress rich visual context into incomplete structural signals. Furthermore, standard reconstruction objectives fail to fully capture try-on-specific human preferences. To address these challenges, we propose InstructVVT, an instruction-driven and reference-guided video virtual try-on framework based on a Diffusion Transformer (DiT) that operates without inference-time spatial priors. Our core insight is to recover fine-grained control directly from the input triplet (source video, reference garment, and instruction) via a dual-level reference conditioning scheme. Specifically, an MLLM infers semantic edit tokens for target disambiguation and structural preservation, while a lightweight conditioning pathway explicitly injects fine-grained visual garment details. Finally, we design a try-on-specific reward and utilize the DiffusionNFT algorithm to align the model with human preferences. Extensive experiments on ViViD-S and TripVVT-Bench demonstrate that InstructVVT outperforms state-of-the-art open-source methods in garment fidelity, structural preservation, and temporal consistency, despite requiring fewer inference-time controls.

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Dingbao Shao, Song Wu, Xinyu Chen, Qian Wang, Jiahang Li, Kuai Jiang, Jiang Lin, Yuhang Liu, Ziyu Chen, Duo Li, Jiaxin Hu, Shengrong Gu, Ziheng Tang, Rongrong Liu, Yanlun Peng, Liang Li, Junlan Feng, Lujia Jin, Ting Zhang, Jian Yang, Zili Yi. 2026-08-14. InstructVVT: Instruction-Driven Video Virtual Try-On without Auxiliary Spatial Priors. https://arxiv.org/abs/2608.14070

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