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ViViD: Video Virtual Try-on using Diffusion Models

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arxiv 2405.11794 v2 pith:HYUVHGAL submitted 2024-05-20 cs.CV

Zixun Fang , Wei Zhai , Aimin Su , Hongliang Song , Kai Zhu , Mao Wang , Yu Chen , Zhiheng Liu
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Yang Cao Zheng-Jun Zha
This is my paper · ORCID
classification cs.CV
keywords videotry-onvirtualclothingdiffusionmodelvividdataset
verification ladder T0 review T1 audit T2 compute T3 formal
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Video virtual try-on aims to transfer a clothing item onto the video of a target person. Directly applying the technique of image-based try-on to the video domain in a frame-wise manner will cause temporal-inconsistent outcomes while previous video-based try-on solutions can only generate low visual quality and blurring results. In this work, we present ViViD, a novel framework employing powerful diffusion models to tackle the task of video virtual try-on. Specifically, we design the Garment Encoder to extract fine-grained clothing semantic features, guiding the model to capture garment details and inject them into the target video through the proposed attention feature fusion mechanism. To ensure spatial-temporal consistency, we introduce a lightweight Pose Encoder to encode pose signals, enabling the model to learn the interactions between clothing and human posture and insert hierarchical Temporal Modules into the text-to-image stable diffusion model for more coherent and lifelike video synthesis. Furthermore, we collect a new dataset, which is the largest, with the most diverse types of garments and the highest resolution for the task of video virtual try-on to date. Extensive experiments demonstrate that our approach is able to yield satisfactory video try-on results. The dataset, codes, and weights will be publicly available. Project page: https://becauseimbatman0.github.io/ViViD.

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Forward citations

Cited by 10 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. TryOnCrafter: Unleashing Camera Trajectories for Realistic Video Virtual Try-on via a Renderable 4D Try-on Proxy

    cs.CV 2026-06 unverdicted novelty 7.0 of 10

    TryOnCrafter is the first DiT-based framework for camera-controllable video virtual try-on via a renderable 4D try-on proxy distilled from 2D priors into 3DGS avatar animated with SMPL-X.

  2. UniVVT: A Unified End-to-End Framework for High-Fidelity Video Virtual Try-on

    cs.CV 2026-08 conditional novelty 6.0 of 10

    UniVVT reports state-of-the-art video and image virtual try-on by conditioning a diffusion video generator on task tokens from a multimodal language model, with no masks, poses, or warping at inference.

  3. Vid-CamEdit: Video Camera Trajectory Editing with Generative Rendering from Estimated Geometry

    cs.CV 2025-06 conditional novelty 6.0 of 10

    Vid-CamEdit re-synthesizes monocular videos along user-defined camera paths by conditioning a video diffusion model on 2D flows derived from estimated 3D geometry, without training on multi-view video data.

  4. Low-Barrier Dataset Collection with Real Human Body for Interactive Per-Garment Virtual Try-On

    cs.GR 2025-06 conditional novelty 6.0 of 10

    A per-garment virtual try-on pipeline that trains a GAN from a two-minute real-human video capture and uses a hybrid pose-plus-DensePose input to synthesize the garment with accurate alignment.

  5. Pursuing Temporal-Consistent Video Virtual Try-On via Dynamic Pose Interaction

    cs.CV 2025-05 conditional novelty 6.0 of 10

    DPIDM, a diffusion model with pose-aware spatial and temporal attention plus a temporal attention loss, reports state-of-the-art video virtual try-on and cuts VFID on VVT from 1.280 to 0.506.

  6. Real-Time Per-Garment Virtual Try-On with Temporal Consistency for Loose-Fitting Garments

    cs.GR 2025-06 conditional novelty 5.0 of 10

    A per-garment virtual try-on method for loose-fitting garments uses a garment-invariant pose representation and a recurrent ConvLSTM synthesis network to achieve temporally smoother try-on video at about 10 fps.

  7. ChronoTailor: Harnessing Attention Guidance for Fine-Grained Video Virtual Try-On

    cs.CV 2025-06 conditional novelty 5.0 of 10

    ChronoTailor combines region-aware attention guidance, temporal feature fusion, and multi-scale garment-pose alignment to produce state-of-the-art video virtual try-on results, and contributes the StyleDress dataset.

  8. OmniV2V: Versatile Video Generation and Editing via Dynamic Content Manipulation

    cs.CV 2025-06 conditional novelty 5.0 of 10

    OmniV2V is one diffusion-transformer model that performs eight video generation and editing tasks by combining mask, pose, image, and text-instruction conditions.

  9. Human Motion Video Generation: A Survey

    cs.CV 2025-09 conditional novelty 4.0 of 10

    A comprehensive survey with a five-phase pipeline model for human motion video generation, covering over 200 papers and adding a new benchmark comparison of nine pose-guided methods.

  10. TalkFashion: Intelligent Virtual Try-On Assistant Based on Multimodal Large Language Model

    cs.CV 2025-07 conditional novelty 4.0 of 10

    TalkFashion, a text-driven virtual try-on assistant, reports better semantic consistency and visual quality than four baselines on VITON-HD by combining an LLM router, catalog matching, and automatic mask generation.

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