AvatarVTON: 4D Virtual Try-On for Animatable Avatars

• URL: http://arxiv.org/abs/2510.04822v1
• Date: Mon, 06 Oct 2025 14:06:34 GMT
• Title: AvatarVTON: 4D Virtual Try-On for Animatable Avatars
• Authors: Zicheng Jiang, Jixin Gao, Shengfeng He, Xinzhe Li, Yulong Zheng, Zhaotong Yang, Junyu Dong, Yong Du,
• Abstract summary: AvatarVTON generates realistic try-on results from a single in-shop garment image.<n>It supports dynamic garment interactions under single-view supervision.<n>It is well-suited for AR/VR, gaming, and digital-human applications.
• Score: 67.13031660684457
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose AvatarVTON, the first 4D virtual try-on framework that generates realistic try-on results from a single in-shop garment image, enabling free pose control, novel-view rendering, and diverse garment choices. Unlike existing methods, AvatarVTON supports dynamic garment interactions under single-view supervision, without relying on multi-view garment captures or physics priors. The framework consists of two key modules: (1) a Reciprocal Flow Rectifier, a prior-free optical-flow correction strategy that stabilizes avatar fitting and ensures temporal coherence; and (2) a Non-Linear Deformer, which decomposes Gaussian maps into view-pose-invariant and view-pose-specific components, enabling adaptive, non-linear garment deformations. To establish a benchmark for 4D virtual try-on, we extend existing baselines with unified modules for fair qualitative and quantitative comparisons. Extensive experiments show that AvatarVTON achieves high fidelity, diversity, and dynamic garment realism, making it well-suited for AR/VR, gaming, and digital-human applications.

Related papers

• SteadyDancer: Harmonized and Coherent Human Image Animation with First-Frame Preservation [50.792027578906804]
  We introduce SteadyDancer, an Image-to-Video (R2V) paradigm-based framework that achieves harmonized and coherent animation.<n> Experiments demonstrate that SteadyDancer achieves state-of-the-art performance in both appearance fidelity and motion control.
  arXiv  Detail & Related papers  (2025-11-24T17:15:55Z)
• EVA: Expressive Virtual Avatars from Multi-view Videos [51.33851869426057]
  We introduce Expressive Virtual Avatars (EVA), an actor-specific, fully controllable, and expressive human avatar framework.<n>EVA achieves high-fidelity, lifelike renderings in real time while enabling independent control of facial expressions, body movements, and hand gestures.<n>This work represents a significant advancement towards fully drivable digital human models.
  arXiv  Detail & Related papers  (2025-05-21T11:22:52Z)
• SEGA: Drivable 3D Gaussian Head Avatar from a Single Image [15.117619290414064]
  We propose SEGA, a novel approach for 3D drivable Gaussian head Avatar creation.<n>SEGA seamlessly combines priors derived from large-scale 2D datasets with 3D priors learned from multi-view, multi-expression, and multi-ID data.<n>Experiments show our method outperforms state-of-the-art approaches in generalization ability, identity preservation, and expression realism.
  arXiv  Detail & Related papers  (2025-04-19T18:23:31Z)
• UniViTAR: Unified Vision Transformer with Native Resolution [37.63387029787732]
  We introduce UniViTAR, a family of homogeneous vision foundation models tailored for unified visual modality and native resolution scenario.<n>A progressive training paradigm is introduced, which strategically combines two core mechanisms.<n>In parallel, a hybrid training framework further synergizes sigmoid-based contrastive loss with feature distillation from a frozen teacher model.
  arXiv  Detail & Related papers  (2025-04-02T14:59:39Z)
• VTON 360: High-Fidelity Virtual Try-On from Any Viewing Direction [103.0918705283309]
  Virtual Try-On (VTON) is a transformative technology in e-commerce and fashion design, enabling realistic digital visualization of clothing on individuals.<n>We propose VTON 360, a novel 3D VTON method that addresses the open challenge of achieving high-fidelity VTON that supports any-view rendering.
  arXiv  Detail & Related papers  (2025-03-15T15:08:48Z)
• ITVTON: Virtual Try-On Diffusion Transformer Based on Integrated Image and Text [1.7071356210178177]
  ITVTON is an efficient framework that leverages the Diffusion Transformer (DiT) as its single generator to improve image fidelity.<n>ITVTON effectively captures garment and person images along the width dimension and incorporating textual descriptions from both.<n>Experiments on 10,257 image pairs from IGPair confirm ITVTON's robustness in real-world scenarios.
  arXiv  Detail & Related papers  (2025-01-28T07:24:15Z)
• Improving Diffusion Models for Authentic Virtual Try-on in the Wild [53.96244595495942]
  This paper considers image-based virtual try-on, which renders an image of a person wearing a curated garment. We propose a novel diffusion model that improves garment fidelity and generates authentic virtual try-on images. We present a customization method using a pair of person-garment images, which significantly improves fidelity and authenticity.
  arXiv  Detail & Related papers  (2024-03-08T08:12:18Z)
• MonoHuman: Animatable Human Neural Field from Monocular Video [30.113937856494726]
  We propose a novel framework MonoHuman, which robustly renders view-consistent and high-fidelity avatars under arbitrary novel poses. Our key insight is to model the deformation field with bi-directional constraints and explicitly leverage the off-the-peg information to reason the feature for coherent results.
  arXiv  Detail & Related papers  (2023-04-04T17:55:03Z)
• Drivable Volumetric Avatars using Texel-Aligned Features [52.89305658071045]
  Photo telepresence requires both high-fidelity body modeling and faithful driving to enable dynamically synthesized appearance. We propose an end-to-end framework that addresses two core challenges in modeling and driving full-body avatars of real people.
  arXiv  Detail & Related papers  (2022-07-20T09:28:16Z)
• Single Stage Virtual Try-on via Deformable Attention Flows [51.70606454288168]
  Virtual try-on aims to generate a photo-realistic fitting result given an in-shop garment and a reference person image. We develop a novel Deformable Attention Flow (DAFlow) which applies the deformable attention scheme to multi-flow estimation. Our proposed method achieves state-of-the-art performance both qualitatively and quantitatively.
  arXiv  Detail & Related papers  (2022-07-19T10:01:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.