Title: Virtual Fitting Room: Generating Arbitrarily Long Videos of Virtual Try-On from a Single Image
Venue: NeurIPS 2025
Accepted: yes
Keywords: Virtual Try-On

Video Diffusion Models

Generative Models
Abstract: This paper proposes Virtual Fitting Room (VFR), a novel video generative model that produces arbitrarily long virtual try-on videos. Our VFR models long video generation tasks as an auto-regressive, segment-by-segment generation process, eliminating the need for resource-intensive generation and lengthy video data, while providing the flexibility to generate videos of arbitrary length. The key challenges of this task are twofold: ensuring local smoothness between adjacent segments and maintaining global temporal consistency across different segments. To address these challenges, we propose our VFR framework, which ensures smoothness through a prefix video condition and enforces consistency with the anchor video — a 360°-view video that comprehensively captures the human's whole-body appearance. Our VFR generates minute-scale virtual try-on videos with both local smoothness and global temporal consistency under various motions, making it a pioneering work in long virtual try-on video generation. Project Page: https://immortalco.github.io/VirtualFittingRoom/.


Peer reviews:
comments: SUMMARY:
This paper introduces Virtual Fitting Room (VFR), a novel video generative model capable of synthesizing arbitrarily long virtual try-on videos. VFR generates extended videos autoregressively by producing segments sequentially, ensuring temporal coherence. To maintain smoothness and consistency, the model conditions each segment on both a prefix video (previously generated content) and an anchor video (reference appearance). Experimental results demonstrate that VFR surpasses existing baselines in performance.

STRENGTHS AND WEAKNESSES:
Strengths:
1. The paper is well written and easy to follow.
2. The idea of using an anchor video to improve consistency is effective and interesting.

Weaknesses:
1. The technical novelty of the proposed method appears limited, as it primarily combines existing techniques.
2. I don't think it is appropriate to claim "implicit 3D-aware" generation, which also appears confusing in Figure 4. Any well-trained video generative model inherently exhibits such properties to some degree.

QUESTIONS:
None.

LIMITATIONS:
yes.
TITLE: Official Review
CLARITY: 3
IMPACT: 2
ORIGINALITY: 2
DATE: 2025-06-26

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents Virtual Fitting Room (VFR), an auto-regressive video generation framework capable of synthesizing minute-scale virtual try-on videos. By generating videos segment-by-segment, VFR avoids the computational burden of end-to-end long video synthesis. It addresses key challenges—local smoothness and global temporal consistency—via prefix video conditioning and an anchor 360° video capturing full-body appearance. The method demonstrates good performance under diverse motions.\n\nSTRENGTHS AND WEAKNESSES:\nStrength：\n1. An auto-regressive virtual try-on method is proposed, capable of generating temporally consistent videos of arbitrary length.\n2. A 3D prior is introduced as an anchor video, enabling the generation of 360-degree videos with 3D consistency.\n\n\nWeakness:\n1. The motion of garments appears overly constrained by the underlying 3D human model in the anchor videos （might be SMPL). For instance, loose clothing exhibits minimal dynamics or moves as a rigid body, failing to realistically simulate cloth-specific behaviors such as the flow of skirts.\n2. Several implementation details are unclear. It appears that the anchor video is generated by the proposed VFR model conditioned on a given A-pose. However, Section B.1 of the supplementary material only mentions conditioning on the prefix video and anchor video for DIT. It remains ambiguous how the initial anchor video is produced.\n3. The paper does not describe the process of fusing a target user with a specified garment to produce the initial try-on appearance, which is crucial for practical applicability.\n4. The inference pipeline lacks clarity regarding the generation of the first frame and the initial prefix video. It is important to clarify whether these are produced by a separate model or integrated into the VFR pipeline, and how much they affect overall video quality.\n\nQUESTIONS:\n1. The necessity and efficiency of the repeated refinement steps are not well justified. It would be helpful to report how the number of refinement iterations is determined, the computational overhead introduced, and the potential performance impact if the refiner is applied to other datasets such as Dress&Dance.\n2. During the generation process, how much time is spent on the VFR module versus the refiner?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces Virtual Fitting Room (VFR) for creating arbitrarily long, high-resolution virtual try-on videos from a single source image of a person and a garment. The method operates auto-regressively, generating the video in sequential segments. To address the key challenges of this approach, the authors propose two core mechanisms: (1) a \"prefix video\" condition to ensure local smoothness and seamless transitions between adjacent video segments, and (2) an \"anchor video,\" a 360° rendering of the subject in an \"A\" pose, which serves as a global reference to maintain temporal consistency of the person and garment's appearance throughout the long video. A notable finding is that by enforcing temporal consistency, the model implicitly learns 3D structure, enabling free-viewpoint rendering without any 3D supervision.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n1. The paper presents a method to generate minute-scale, high-resolution (720×1152) virtual try-on videos from a single image, which the authors claim is the first of its kind. The proposed \"anchor video\" concept is an intuitive and effective solution to the critical problem of maintaining global temporal consistency in auto-regressively generated long videos. \n\n2. A significant finding is that the model learns 3D-consistent representations as a by-product of enforcing temporal consistency, allowing for 3D reconstruction and free-viewpoint rendering without explicit 3D data. \n\n**Weaknesses**\n\n1. The paper does not contain any quantitative comparisons against baseline methods in the main text. The only quantitative results in the supp. mat. is actually an ablation study. So it is questionable whether this paper is suitable as a submission to NeurIPS? Maybe it will better to submit it to Siggraph or other conferences.\n\n2. The model is trained on a dataset named \"Dress&Dance\" which is cited as being \"Under Review\" and linked to an anonymous URL.  This makes it unclear whether the strong qualitative results stem primarily from the proposed VFR method or from the quality of the specific, inaccessible training data. Since the dataset is cited separately, the reviewer will assume that this paper has no dataset contribution claim. In this context, it is less persuasive that the proposed method contributes to the final performance rather than the private data. Besides, the anonymous URL is broken so the reviewer cannot download pdf files containing descriptions of the dataset.\n\n3. Since the anchor video in this method is vital, it is also unclear how the proposed method could ensure or improve 3D consistency in its training. This weaknesses is also related to the second one, where the authors fail to elaborate the details of training set, therefore it is unclear how they promote the video quality and 3D consistency when they train the model to generate the anchor video.\n\nQUESTIONS:\nPlease see the weaknesses.\n\nI think this paper's scope is not suitable for NeurIPS, and it will also be better to conduct the experiments on public benchmarks instead of private dataset that is not publicly available, given that this paper has no dataset contribution claim.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 2, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces the Virtual Fitting Room (VFR), a novel framework for generating arbitrarily long virtual try-on videos from a single user image and a garment reference, which uses an auto-regressive generation process guided by an anchor video to ensure global temporal consistency and local smoothness. Experiments show the efficiency of the proposed method.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. This paper claim that they are the first method that can generate arbitrarily long, high-resolution human videos of virtual try-on from a single image. Thus this paper is somewhat innovative in the field of try-on.\n2. The proposed evaluation protocol covers multiple aspects of virtual try-on quality, including garment consistency, human-garment interaction, and robustness to arbitrary poses. This provides a standardized way to assess future work in the field.\n3. The visual results in the paper and supplementary demonstrate the effectiveness of the proposed method\n\nWeakness:\n1. Although this paper has made some progress in the try-on field, the overall technical contribution is still limited. This AR video generation way has also been used in other tasks using video generation model, like StarGen [1]. Regarding the video AR generation, the article can also discuss and compare the AR video generation solution in LCT [2], which can use some KV cache to speed up. \n2. I am quite confused about how to get the anchor video during inference, which is a 360 video in the paper. But during training, two clips are sampled from one video. Will there be a gap between the anchor video during training and testing if they are too different\n3. The refinement process is not elegant and needs to be performed multiple times, which consumes computing power. Is there a more elegant and end-to-end approach?\n\n[1] StarGen: A Spatiotemporal Autoregression Framework with Video Diffusion Model for Scalable and Controllable Scene Generation\n[2] Long Context Tuning for Video Generation\n\nQUESTIONS:\nPlease refer to weaknesses.\n\nLIMITATIONS:\nMost limitations have been declared by the authors.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-03"}