Title: OmniTry: Virtual Try-On Anything without Masks
Venue: NeurIPS 2025
Accepted: yes
Keywords: Virtual Try-On

Controllable Image Generation

Diffusion Model
Abstract: Virtual Try-ON (VTON) is a practical and widely-applied task, for which most of existing works focus on clothes. This paper presents OmniTry, a unified framework that extends VTON beyond garment to encompass any wearable objects, e.g., jewelries and accessories, with mask-free setting for more practical application. When extending to various types of objects, data curation is challenging for obtaining paired images, i.e., the object image and the corresponding try-on result. To tackle this problem, we propose a two-staged pipeline: For the first stage, we leverage large-scale unpaired images, i.e., portraits with any wearable items, to train the model for mask-free localization. Specifically, we repurpose the inpainting model to automatically draw objects in suitable positions given an empty mask. For the second stage, the model is further fine-tuned with paired images to transfer the consistency of object appearance. We observed that the model after the first stage shows quick convergence even with few paired samples. OmniTry is evaluated on a comprehensive benchmark consisting of 12 common classes of wearable objects, with both in-shop and in-the-wild images. Experimental results suggest that OmniTry shows better performance on both object localization and ID-preservation compared with existing methods. The code, model weights, and evaluation benchmark of OmniTry will be made publicly available. The code, model weights, and evaluation benchmark of OmniTry are available at https://omnitry.github.io/.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents OmniTry, a virtual try-on framework designed for various wearable objects, featuring a two-stage training process. It is first trained on a large amount of unpaired data, followed by fine-tuning with a small set of paired data, effectively addressing the challenge of obtaining paired images. In addition, OmniTry adopts a mask-free setting, eliminating the need for additional mask-based guidance. Extensive experiments show the superiority of the method in terms of both generation quality and generalization ability.\n\nSTRENGTHS AND WEAKNESSES:\n***Strengths:***\n1. The paper breaks the conventional limitation of virtual try-on methods to specific target categories and proposes a more general framework.\n2. The paper presents diversity and high-quality experimental results.\n\n***Weaknesses:***\n1. The reliance on multiple preprocessing steps such as text description generation, segmentation, and object erasing may lead to significant error accumulation, which could negatively affect the final performance of the model.\n2. It is confusing that the final output of Traceless Erasing is a processed try-on image, while the information leakage problem is initially attributed to the erased image. The training configuration following the traceless erasing step is not clearly described.\n3. The description of the mask in Figure 4 as \"optional\" appears ambiguous, implying that OmniTry can also generate results based on these masks. Additionally, corresponding text descriptions are not provided in the samples.\n4. Example results showcasing varying transfer effects on the same object, as described in line 126, are missing from the paper.\n5. Typo found at Line 112.\n\nQUESTIONS:\nIt is noted that object images are mostly collected from in-the-wild sources. How does the presence of multiple objects in one image affect the results? For example, in the last case shown in Figure 1, the object image contains a hat, but it is not transferred to the final result.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-19"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents OmniTry, a unified mask-free framework for virtual try-on (VTON) across a wide range of wearable objects beyond garments, such as accessories and jewelry. The authors propose a two-stage training pipeline: the first stage learns mask-free localization using unpaired images and object descriptions, while the second stage introduces a small amount of paired data to enforce object identity consistency. A comprehensive benchmark covering 12 object categories is constructed to evaluate performance under both in-shop and in-the-wild conditions.\n\nThe paper demonstrates a substantial engineering effort and addresses a practically valuable problem in the field of image-based try-on systems. The breadth of supported object types, the construction of a new evaluation benchmark, and the overall system integration reflect a significant amount of implementation and empirical work.\n\nHowever, I have concerns regarding the novelty (Section 3.2: Unpaired Data Pre-process, Model Architecture) and the effectiveness (Section 3.3: Stage-2: ID Consistency Preservation) of the proposed approach. Incorporating more comparative experiments with existing methods, as well as ablation studies on the proposed components, would make the paper more complete.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\nQuality:\n\n1.This paper proposes a unified mask-free virtual try-on framework for multiple object categories, which demonstrates good usability and try-on quality.\n\n2.The engineering implementation is thorough and well-documented, providing useful insights for the virtual try-on task. \n\n3.This paper introduces a benchmark that evaluates virtual try-on models from multiple perspectives, contributing to the advancement of the try-on research community.\n\nWeaknesses:\n\nOriginality:\n\n1.The unpaired data construction method described in Section 3.2 (Unpaired Data Pre-process) is a commonly used approach in recent literature, corresponding to the \"Add\" operation in image editing tasks. Similar strategies can be found in AnyEdit (CVPR 2025), UniReal (CVPR 2025), and OmniGen (CVPR 2025). Including comparisons with these methods could better highlight the distinctiveness of the proposed approach.\n\n2.The Inpainting Model presented in line 133 under Model Architecture is a widely adopted backbone for subject-driven image editing. Similar architectures are used in In-Context-LoRA, as well as in references [1] and [2]. I suggest adding comparisons with methods based on similar structures to more clearly demonstrate the differences and advantages of your design.\n\nClarity & Significance:\n\n1.Figure 5 does not indicate the specific training cost of the first stage. Moreover, the figure shows that comparable performance can be achieved using only 200 samples without any first-stage training. Considering the additional computational cost of the first stage, it seems plausible that training with only the second stage could also be acceptable.\n\n2.Building upon the point above, the necessity of the Two-Stream Adapter design is unclear. It may be feasible to jointly train on unpaired and paired samples using only a single LoRA adapter. Additionally, for the one-stream adapter ablation in Table 2, the parameter count of the one-stream adapter should be reported. It is important to ensure that the one-stream and two-stream setups are comparable in both parameter size and training effort.\n\n3.Table 2 shows that the best LPIPS score is achieved without using full attention. What explains this phenomenon? The explanations in lines 288–289 are insufficiently supported. I recommend adding more attention visualizations and qualitative results to clarify the role and effectiveness of full attention.\n\nReference:\n\n[1] Large-Scale Text-to-Image Model with Inpainting is a Zero-Shot Subject-Driven Image Generator. (CVPR2025)\n\n[2] Flux Already Knows – Activating Subject-Driven Image Generation without Training.\n\n[3] In-Context-LoRA: IN-CONTEXT LORA FOR DIFFUSION TRANSFORMERS https://github.com/ali-vilab/In-Context-LoRA \n\n[4] AnyEdit: Mastering Unified High-Quality Image Editing for Any Idea.\n\n[5] UniReal: Universal Image Generation and Editing via Learning Real-world Dynamics.\n\n[6] OmniGen: Unified Image Generation.\n\nQUESTIONS:\n1.How does the unpaired data construction process proposed in this work differ from the \"Add\" operation unpaired data construction commonly used in existing image editing methods, such as AnyEdit, UniReal, and OmniGen? Could the authors include comparative experiments to better highlight the uniqueness of their strategy?\n\n2.Given that the inpainting model architecture closely resembles existing subject-driven image editing frameworks such as In-Context-LoRA, what are the specific innovations in the proposed model design? Would it be possible to add comparisons with structurally similar baselines?\n\n3.Is the Two-Stream Adapter design necessary, considering that the second-stage model alone achieves comparable performance with sufficient paired samples? Could a unified single-adapter design be equally effective, and if so, has the parameter efficiency been fairly compared in Table 2?\n\n4.Why does disabling full attention lead to better LPIPS scores in Table 2? The explanation provided in lines 288–289 is insufficiently supported. Can the authors include attention visualizations and additional qualitative analysis to clarify the impact of full attention?\n\nLIMITATIONS:\n1.This work still does not address the critical issue of how clothing size affects the try-on results, which remains a key challenge in virtual try-on.\n\n2.The proposed method may experience performance degradation in scenarios where the image contains multiple persons or when occlusions are present.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-27"}

comments: SUMMARY:
This paper proposes a unified framework that enables virtual try-on of multiple wearable objects. Specifically, the proposed method first uses large-scale unpaired images to perform prior learning. This step is essentially a detect-erase-restore process, where multi-modal large language models are introduced to provide descriptions of all wearable items as generative guidance. Then, the model is further fine-tuned using paired data conditioned on the corresponding wearable object images.

STRENGTHS AND WEAKNESSES:
**Strengths**
1.  This paper proposes a novel framework that supports the virtual try-on for 12 common classes of wearable objects, which is an interesting and sound idea.
2.  This paper is well-structured, and the experiments are clearly described.

**Weaknesses**
1. Although the design of the framework is technically sound, the use of multiple preprocessing steps and pre-trained models makes the overall pipeline complex and computationally expensive. The trade-off between the effectiveness and efficiency needs to be further considered.
2. The paper attributes the perfect reconstruction in erased samples to information leakage. Is there sufficient evidence to support this claim? Could it also be caused by other factors, such as overfitting to the training data?
3. The example in Figure 3(b) does not clearly demonstrate the effectiveness of Traceless Erasing. Could the authors provide more convincing results, such as the necklace case in Figure 3(a)?

QUESTIONS:
See “Weaknesses” in Review.

LIMITATIONS:
Yes.
TITLE: Official Review
CLARITY: 3
IMPACT: 2
ORIGINALITY: 3
DATE: 2025-06-30

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nOmniTry presents a unified, mask-free virtual try-on framework that extends beyond garments to any wearable object via a two-stage training pipeline: Mask-free localization on large-scale unpaired portrait images, using MLLM-guided object description, object removal, and an inpainting-based diffusion transformer repurposed to learn where and how to paint objects. ID-consistency fine-tuning on paired in-shop images, introducing an identity adapter and masked full-attention to preserve object appearance 4326\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n1. To my knowledge, it is the first framework to support any wearable object, not just clothing.\n2. The two-stage training pipeline leverages unpaired real-world images for localization, improving its generalization performance. \n3. The results compared with current works seems to be reasonable.\n\nWeakness\n1. This work involves many modules or strategies, like two-stage training, but the core contributions and novelties are not clearly presented. It seems it is a combinations of variant modules and lack original novelty. \n2. Any2AnyTryon seems to address a similar task. The relationship and differences with this work should be presented. \n3. It depends on external modules. The reliance on MLLM for object descriptions and erasing models means that errors in these stages can affect the final output, leading to potential inaccuracies.\n4. The paper does not include a human evaluation to assess how realistic or useful the generated try-ons are, especially when compared to garment-only methods.\n\nQUESTIONS:\n1. Can OmniTry adapt to novel wearable categories that were not part of the training set, and how many paired examples are necessary for effective adaptation?\n2. How does the model perform when the descriptions generated by the MLLM are incorrect or noisy? Is there a degradation in localization accuracy or identity preservation?\n\nLIMITATIONS:\nSee Weakness and Questions", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-30"}