Virtual Fitting Room: Generating Arbitrarily Long Videos of Virtual Try-On from a Single Image
Authors: Jun-Kun Chen
Aayush Bansal
Minh Phuoc Vo
Yu-Xiong Wang
Venue: NeurIPS 2025 poster ()
Keywords: Virtual Try-On
Video Diffusion Models
Generative Models
Primary area: deep_learning
TL;DR: We propose VFR that generates minute-scale long virtual try-on videos at high resolution.

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/.

Virtual Fitting Room: Generating Arbitrarily Long Videos of Virtual Try-On from a Single Image
Jun-Kun Chen 1.+
Aayush Bansal
Minh Phuoc Vo!
Yu-Xiong Wang
SpreeAl
Uniiversity of Illinois Urbana- Urbana-Champaige Champaign
immortalco. gi thub. io it/Urrtuallitttingooo
Abstract
We introduce Virtual Fitting Room (VFR), novel video generative model that produces arbitrarily long virtual try-on videos. Our VFR models long video generation tasks as auto-regressive, segment-by-segment generation process, eliminating the need for resource-intensin 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 prefix video condition and enforces consistency with the anchor video-a 360 video that comprchensively 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 pioneering work in long virtual try-on video generation.
Introduction
Imagine being in a fitting room, trying on garment, when a hurried knock interrupts you. Would that allow you to truly experience the garment before buying it? No. To truly understand a garment, one may want to interact with it in various ways. The computational methods for virtually trying on garment enable
themselves, but only in an image [1-6] short 5~10ss video [7, 8], limiting the user's ' ability to fully experience garment. We introduce Virtual Fitring Room (VFR) to enable a user to study the interaction of garments with their body long as they like. Unlike existing image or video try-on methods, VFR allows a user to create arbitrarily long videos (720 x 1152 resolution at 8 FPS and can be further refined 24 FPS) of themselves, given single user image, desired garment, and reference video performing the desired try-on motion. Fig. shows a 30s and a 90s video generated using our method.
Generating 5s-long video is already a computationally demanding task [7, 8J. Naively extending these methods requires even more computational resources and large-scale video dataset containing long videos for learning. To overcome these limitations, one may generate multiple short segments of a long video one by one "aguto-rggressvvel""" in timestamp order, and then merge them to create long video, as visualized in Fig. 4-(a). Inspired by common approaches in general long video generation [9-14], we allow each segment slightly overlap with the previous segment, and pass the overlapped "prefix" of the current segment
condition, ensuring a locally smooth transition between each adjacent segment pair. However, these generated videos lack global temporal consistency, as shown in Fig. 2-(a), which is difficult to fix after once they are generated. In this work, we draw inspiration from the process of writing an essay with an outline as an "anchor." We posit that creating long videos is a two-step process that involves: (I) creating an "outline" or an "anchor" to guide the
Work done during internship at SpreeAI.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Figure 1: We generate two arbitrarily long videos: (a) 30s video; and (b) 90s video, for user interacting with a given garment. Our approach preserves accessories glasses and slippers, and allows desirable user-garment interaction. Please refer the project page for full streaming.
generation; and (2) generating multiple short vidcos that are consistent with the anchor. We observe that short 360 video like Fig. 3-(a) of the human subject in simple "A" pose serves as reasonable anchor, allowing the model to comprehensively design the whole-body appearance of the human. The multiple short video segments are consistent with the anchor video and, therefore, are also consistent with each other. With the anchor video, our generated long video achieves temporal consistency (Fig. 2-(b)) without requiring long videos training.
Evaluating the quality of long video virtual try-on. With the flexibility to generate arbitrarily long videos, we introduce an evaluation protocol assess performance across four aspects with varying difficulty levels: (1) 360 Garment Consistency subject in "A" pose, which allows
360" video of a stationary human study the quality of the generated garment (Fig. 3-(a)); (2) 30s Harmn+Garment Consistency a 30s video of human subject casually moving around point in front of stationary camera, which enables us assess the quality of the generated human and garment (Fig. I-(a)); (3) Hand-Body Interaction Faithfulness a 90s video of human subject performing a fixed set of poses front of stationary camera, which facilitates the evaluation of the robustness of the virtual try-on method in controlled settings (Fig. 1-(b)); and (4) Capability for Arbitrary Poses 30~60s video human subject freely interacting with their body, which allows us to investigate robustness in various poses and orientations. We believe that this evaluation protocol will enable us to comprehensively assess the quality of virtual try-on methods.
Free viewpoint rendering is for free. A by-product of learning temporally consistent video is that we can render human subject in any pose and viewpoint. Fig. 3-(a) shows a generated 360 anchor video ("A" pose) of a user wearing the target garment. The output is 3D consistent, enabling us to reconstruct into a mesh as visualized Fig. 3-(b) in a NeRFStudio [15] viewer. We observe that 3D implicitly emerges while enforcing temporal consistency. Interestingly, we can recloth and reanimate any human subject from a single image, and view them from any viewpoint.
Our Contributions. (1) We introduce VFR, the first method to generate arbitrarily long, high- resolution (720 1152 resolution 24FPS) human videos of virtual try-on from single image. (2) We also introduce an evaluation protocol to assess the overall quality of virtual try-on methods. (3) We observe that the proposed method implicitly learns 3D consistency, enabling us to perform free-viewpoin rendering. Though designed for long video virtual try-on, our approach also provides insights for general long video generation, where the anchor mechanism offers a simple way to maintain global consistency over time.
2 Related Work
We believe static 2D imagery isn') enough for realistic virtual try-on experience. The challenge of achieving high-resolution virtual try-on escalates as we progress from images to videos, and ultimately to 4D. This progression not only heightens the demand for computational resources, but

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User
(a) Conventional auto-Regressive Models: Temporal Inconsistent
Garment
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Figure 2: Given a user and target garment, (a) conventional auto-regressive video generators suffer from temporal inconsistency issues between distant frames. Note different patterns of sleeves in red bounding boxes across the time. (b) Our VFR generates temporally consistent try-on videos.
(a) 360"-View Anchor Video
(b) Reconstructed 3D Human
Figure 3: (a) VFR produces a 360 anchor video ("A" pose) of a user for a given garment. We observe that the outputs are 3D-consistent, allowing to (b) reconstruct it into a 3D human mesh.
also diminishes the availability of previous extensive databases necessary for learning. Our goal is to identify computational methods that enable the generation of arbitrarily long videos, even with constrained resources.
Image-to-Image Try-on. Given an image of the user and a reference to the target garment, the goal here is to synthesize new image of the user wearing the target garment [16-37, 4, 38-40]. Most methods take two-step approach: (1) deform the garment for given user, known as warping; and (2) generate a new image with the deformed garment using GAN [41] or latent diffusion models [42-44]. A few exceptions [20, 33, 45] generate the output without an intermediate warping step. The primary limitation in this field the restricted experience a user can have with garments. One can only see them in exactly the same pose as in the input image. This issue can potentially be addressed by incorporating an additional module that can synthesize humans in different poses [46-51]. However, due to error propagation, the garment's appearance becomes inconsistent. Consequently, various methods [52-56] aim to jointly change the pose and the garment. These methods, however, lack temporal consistency and smoothness when applied across series of poses.
Video-to-Videss Try-on. Given a video of the user and reference to the target garment, the objective here is to synthesize new video of the user wearing the target garment [57-62]. An important distinction is ensuring that the synthesized garments and humans are temporally consistent and accurate. He et al. [63] employs an image-to-image try-on methodology, but incorporates an additional temporal loss during training to enforce consistency. Recent methods [7, 8] can generate high-resolutie output, but they limited by the duration of the generated video (5s). Naively increasing the duration of videos will require enormous computational resources.
Image-to-Videe Video Try-on. In this work, we explore the generation of arbitrarily long videos from single user image and garment images. A naive approach is utilize a single image try-on method and animate it using an image-to-video creation module [64-76]. However, a modular approach leads to error propagation, which degrades the quality of the generated outputs. Therefore, we seek an end-to-end video generation pipeline that allows us to preserve the details of the garment [8, 77]. We observe that text conditioning cannot effectively capture long and subtle movements [8]. Instead, use example videos reference to guide the creation of new videos. A notable prior work.
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Seg.
Anehor Video
Anchor Video Template S
Proxy-3D-Aware Video Generation
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50 Racoearaation
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Anchor Video
Final Long Video
VFR Video Generator
Generated Segment
Prefix Video
Anto Regressive Video Generation
Video Generator Modeling
Figure 4: (a) Our VFR is an auto-regressive framework that generates long video segment-by- segment. (b) The video generator takes both an anchor video and a prefix video as input, and generates segment that continues the prefix video while maintaining consistency with the anchor video.
Dress&Dance (DnD) [8], generates 5s videos with high resolution 720 x 1152 at 24FPS. In this work, we generate arbitrarily long videos from single user image and the reference garments.
Long Video Generation. There also a line work [9-14] that investigates long video generation for text-to-video and mage-to-vided tasks. The common idea behind these methods is to introduce an additional "memory back" or "history tracking" mechanism to ensure consistency with the previous frames in typical auto-regressive generation process. For example, concurrent work, FramePack [14] designs a computationally efficient way to consider all the previous frames as conditions when generating new frame. Similarly, StarGen [78] proposes a spatio-temporal
autoregression framework for static scene synthesis, while LCT [79] introduces a long-context
tuning strategy that leverages interleaved 3D positional embeddings and KV-cache mechanisms to extend temporal receptive fields. These approaches can generate smooth long videos; however, their temporal consistency is not guaranteed and often violated due ineffective history tracking or over-compressed memory. In contrast, our VFR method tackles this problem by designing anchor video conditioning mechanism tailored for virtual try-on tasks, providing global appearance prior that promotes strong temporal consistency across the entire long video.
Free Viewpoint Rendering. Our ability generate arbitrarily long videos implicitly allows perform free-viewpoin rendering of humans [80, 81]. We observe that a model trained to capture consistent temporal characteristics implicitly learns 3D consistency in its outputs. This analysis could potentially pave the way for 4D try-ons in the future.
3 VFR: Methodology
Given a user image, a reference garment image, an optional text prompt, and a long motion reference video, our VFR is method that generates long, minute-scale, high-quality try-on videos of the user wearing the desired garment and performing the indicated motion, in an auto-regressive, segment-by- segment manner. In auto-regressive generation framework, the core challenge is to achieve both (1) local smoothness such that the video transitions seamlessly without noticeable sudden changes or morphing (Fig. 5); and (2) global temporal consistency such that the appearance of both the user and the garments at all occurrences in the video is the same (Fig. 2).
To address these crucial challenges, our key insight is to (1) propose the "anchor video" generation to ensure a consistent appearance throughout the entire video, and (2) introduce the video prefix condition and immediate refiner to enhance video smoothness through strong conditioning. With both insights, our VFR achieves high-quality long try-on videos, while ensuring both smoothness and temporal consistency.
Regr-Seeee Video Generation. There are two approaches to generating long videos: one can either produce the entire long video in single forward pass, just like generating a short video, or generate the video auto-regressively as multiple shorter segments (Fig. 4-(a).. The former method necessitates significant computational resources during the generation process, as well as extensive long video datasets; whereas the latter requires similar computational resources and training datasets to those used for short video generation.

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(a) Sudden Change
Morphing
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Figure 5: Without our prefix conditioning, the generated video may contain artifacts like (a) sudden changes or (b) morphing, as highlighted in the boxes, which violate the smoothness requirements.
We opt for the second approach and design VFR as an auto-regressive generative model v <- (ic, Uprefix). As seen in Fig. 4-(b), our model generates a short F F-frame video with input conditions e, such that the generated video has Uprefix as its prefix, and then seamlessly continues into the preceding frames. In long video generation, we first generate the initial F frames up G(co, Ø),
and then generate each (F f) segment 2 +- G c2. ((c,,,,- 1 /
using the f overlapped
frames 0/-1 0/- 14-5+1 from the previous segment as its prefix. The reason for using f frames instead of just one frame is to ensure that both the visual appearance and the motion, which requires multiple frames to represent, are smoothly continued into the next segment conditions C₁ for each segment Uú we only need to replace the full motion reference video with its corresponding segment while keeping all other conditions the same. The final generated video is "f
To obtain the different
[f+1.]] (Fig. 4-()). Please refer the Supplementary for the model details.
Up
3.1
Anchor Video for Temporal Consistency
The issue of temporal consistency arises from the ambiguity of the unseen aspects of the garment or the user's appearance. For instance, given only a frontal image of the garment, its back and side views can be interpreted in multiple, yet plausible, ways. This leads to temporal inconsistencies when different appearances are generated in two non-overlapping segments, as shown in Fig. (a).). To ensure [/+1FFF] consistency, a naive approach to use the prefix of the whole video si'he = up + Concat f-1 j=i " as part of the condition c to generate the next segment v, such that the generated v remains consistent with any frame that has appeared in wnde However, this approach negates all the advantages of the auto-regressive generative framework it also incurs significant computational costs and requires long videos for training to effectively handle o.!"..
Our insight is organize the appearance information of garment and user in more effective representation than the whole 1.th Therefore, we introduce the concept of the anchor video Va. which is short Fa-frame video of the user wearing the garment while performing a different motion, ensuring that any part of the user and the garment is visible in at least one of its frames. Given a motion template m,, the generation of the anchor video be modeled as U. +- G(c) [attonnin: Ø), i.e. generating a virtual try-on video with fixed motion My.
We choose m, to be stationary A-pose like Fig. 3-(a), with both arms slightly raised near the sides of the waist and legs naturally apart, under 360e-view camera trajectory. As shown in Fig. 3-(b), such anchor video generated from this m, is already sufficient for reconstructing a 3D mesh representation with NeRFStudio's 's NeRFacto model [15], indicating that this video effectively captures 3D-consistent appearance of the try-on human video. Conditioning the generator this video anchor is sufficient to promote consistency across poses and viewpoints, ie., a proxy-3D-awan video generation, without any explicit 31) representation [82] 3D motion modeling like SMPL. [83]. Since consistency is an equivalence relationship, each vy 's consistency with va implies their consistency with one another. As result, we achieve cross-segment global appearance consistency, which guarantees the temporal consistency of the final generated long video, as shown in Fig. 2-(b).
3.2 Augo-Regeessive Prefix Conditioning for Smoothness
In our auto-regressive generation, we aim to generate the next segment v, such that its first f frames [1.f] precisely match the given Uprefix- If we generate an arbitrary video v; and then replace its first f frames, there may be a sudden change between the f-th and (ʃ + 1)-th frame, resulting in discontinuous video. A straightforward approach is to model this task as "outpainting" the remaining
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Carmant
Figure 6: In the virtual try-on with the 90s hand-body interaction motion, our VFR generates temporally consistent try-on videos. Please refer to the project page for full streaming.
(f- frames, given the first / frames. This allows us to apply training-free impainting/outppinting method, DiffEdit [84], leveraging the same video diffusion model to generate visually smooth video that starts from the given prefix. However, such a generated video may still contain sudden changes (Fig. 5-)a)) and morphing between different appearances (Fig. 5-(b)).
To enforce the video diffusion model to generate smooth videos, we must explicitly consider fixed prefix Uprefix during the generation process. Therefore, demonstrated in Fig. 4-(b), introduce the prefix as a conditioning input. This approach ensures that the model can learn to generate a smooth video segment that seamlessly continues the input prefix.
3.3 Training Long Video Generator with Short Video Data
The introduction of auto-regressive generation focuses on each frame segment of the video, which only requires videos containing at least F frames for training. This approach eliminates the need for lcage-scale, lengthy video training data, which is challenging to obtain, especially for our virtual try-on videos. Each single generation can be modeled represents both try-on conditions (garment and user image, motion reference video, etc.) and the anchor video. For each F-frame segment v in our dataset, we can set Uprefix +- p(l-f] obtain the corresponding input. However, in most cases, we do not have the desired anchor video v, that is exactly the m, we want. Generating m, by ourselves may also inconsistent with the video, e.g.. with different back or side views.
+- G(c, Uprefxx), where (Cuy-on- V.)
To address this issue, our approach does focus on the U at specific my but instead allows the model to learn to leverage v, 's under an arbitrary motion my This ensures that, regardless of the pose of the ug, the video generator can always optimize its usage to produce videos that are consistent with the given Pa. With this modeling, any video segment showing the same person wearing the same garment can be used as an anchor video for the target video v. More specifically, for each Fram-frame raw video data, we can sample two intervals with the same user and garments to construct a training batch: F-frame interval as target v, and another arbitrary length interval to temporally upsample or downsample it to F, frames and use as va. Utilizing this training strategy, we can effectively train the model with only short (F~3F) raw videos, which not significantly longer than F, yet can already yield sufficient variety of different pairs of and V..

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Figure 7: In the virtual try-on with 550 arbitrary motion, our VFR faithfully preserves consistent garment details and human appearance, showcasing various poses with high quality, These results are shown videos our project page.
3.4 Immediate Refinement of the Generated Segments
The robustness of regressiee video generation is significantly more critical than that of end-to- end generation, as artifacts and errors can accumulate and amplify over time. Specifically, given that both anchor and prefix video conditions complex, pixel-unaligned video conditions, observe increasing artifacts in our initial generation results, which may accumulate to severe degradation.
Inspired by Daess&Dance [8], which leverages standalone generator as a "refiner" to temporally upsample the video and remove artifacts, we also design refiner for our generation. In our case, we do not aim to upsample the videos; instead, we focus entirely smoothing. Our refiner can be modeled as v' +- Grefiner (c, Uprefix v), which takes a F-frame video input, along with all the other conditions including Va. obtaining refined F-frame video v'. Such refiner is directly applied to each v, immediately after its initial generation, so we call an "immediate refiner." Additionally, since the imput e and output v' have the same number of frames, we apply the refiner Grefinrr multiple times to the same video to ensure complete artifact removal. The final output segment eú can be expressed as 22 + G (c. u !F-s+1.FF ,G 1 -- ((--++1,11) where & is the number of iterations. With this immediate refinement technique, resolve the issue of degradation over time in long video generation, maintaining high quality from beginning to end.
artifact removal and video
Experiments
4.1 Experimental Settings
Model Training Settings. Our VFR model is built on Dress&Dance [8] with the addition of "prefix video" and "anchor video" CondNets. We train VFR on both Internet and captured datasets from Dance for 10, 000 iterations. Specifically, the immediate refiner is initialized from the 5, 000-th itcration checkpoint the base VFR, and trained for another 5, 000 iterations.
Evaluation Tasks. We evaluate VFR on four different parts: (1) 360 Garment Consistency, to generate 5s -v0e "A" pose video, which also serves as the anchor videos for the other tasks; (2) 360 Human+Garmen
Consistency, to generate 30s 360 casually moving video; (3) 360 Hand- Body Interaction Faithfulness, to generate 90s video with a fixed motion; and (4) Capability for Arbitrary Poses, to generate a 30~60s video with arbitrary motion. These benchmarks are available on our project page. Notably, existing public video try-on datasets VVT [85] and ViViD [61] are not suitable for evaluating our setting, as their spatial resolutions (256x 192 and 832 652x624, respectively) and temporal lengths (typically only few seconds) are insufficient capture the high-resolution, minute-scale, and temporally consistent generation capabilities targeted by benchmark.

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Baselines. We compare our VFR against the baselines FramePack [14], utilizing an "image virtual try-on + image-to-vide to-dee animation" (IVT+12V) procedure that is aligned with Dress&Dance [8]. We also compare with Kling Video 2.0 [86] in a "repeating image-to-video (RI2V) manner mentioned in [14]. We are unable to compare with the following baselines: Stream'22V [13], as only supports 16:9 landscape videos; Caus [12], given that there is no available mage-to-vvdee checkpoint released; TTT [9], since it only supports Tom and Jerry videos; and DiffusionForcing [10] and DistoryGuidance [1]], as they are restricted to videos from their respective trained datasets. As for the image try-on method, we mainly use the two state-of-the-art method, Dress& Dance image try-on [8] and Kling Try-On [86], to generate the first frame of the video. More combinations including other image virtual try-on methods are compared in our Supplementary.
Ablation studies. We also compare our full VFR with the following variants: (1) "No Prefix" (NP), which does not use prefix conditioning, but directly utilizes DiffEdit [84] to outpaint the video with the prefix; (2) "No Anchor" (NA), which does not generate and condition the segment generations on the anchor video; (3) "No Refine" (NR), which skips the immediate refinements after each segment generation; (4) " Dance" (D&D), which does not use either the anchor video or the prefix conditioning, making it equivalent to a training-free method that employs Dress&Dance with DiffEdit for long video generation.
Metrics. Consistent with Dress& Dance [8] and FramePack [14], we utilize GPT [87]-based scores and VBench [88-90] to evaluate our videos. GPT scores can effectively assess the try-on quality from various aspects, leveraging GPT's visual capabilities; VBench introduces a set of metries that comprehensively evaluate the videos from both quality and semantic perspectives.
4.2 Experimental Results and Analysis
We present our qualitative results in Figs. 6,7,8 as images, and on our project page videos.
360 Human+GGammet Consistency (30s)- Fig. 8. As shown Fig. 8-(a), our VFR produces high- quality, long virtual try-on videos. In Fig. 8-(b), our "No Prefix" (NP) variant, due to the global anchor videos, generates results that are comparable to our full VFR. but exhibits some sudden changes, as illustrated on our project page. In contrast, our "No Anchor" (NA) variant's video displays long-term temporal inconsistencies, while our "Dress&Dance training-frre"" (D&D) variant exhibits even greater temporal inconsistencies in both the short and long term. This highlights that both designs in our VFR for local smoothness and global temporal consistency are effective and essential. In Fig. 8-(c), the baseline FramePack [14] produces overall smooth results with long-term inconsistencies, while the appearances deviate significantly in the results produced by the Kling Video 2.0 [86]-based RI2V method. This demonstrates that the virtual try-on tasks are non-trivial and challenging, underscoring our contribution in achieving high-quality results.
Band Interaction Faithfuliness (90s) 1ag-FFigg Fig. 6. The motion in these evaluation tasks encom- passes both human rotation and arm movements. Our VFR faithfully performs the same motion in different virtual try-on tasks, demonstrating the ability to depict the same motion across various garments- - either pants, skirts, or dresses. Even for such long-term video, our VFR still maintains high temporal consistency, which can be observed by comparing the first and the last frame.
Capability for Arbitrary Poses (~50s)- Fig. 7. In these highly challenging tasks, the motions can be arbitrary, encompassing various arm and leg movements, which lead to even more diverse showcases and interactions between garments and users. We observe that our VFR effectively handles these motions and generates high-quality visualizations to depict them. This shows VFR has the capability to generalize to various long vidco virtual try-on tasks.
Quantitative Analysis. The quantitative evaluation comparisons are provided in Table 1. We use the "Subject Consistency." "Background Consistency," and "Motion Smoothness" from VBench [88] to evaluate how the two major challenges - temporal consistency and smoothness - are addressed, as well as the GPT metric in [8] to assess the overall virtual try-on quality. We provide the more quantitative evaluations in Supplementary.
As shown in Table 1, in each component of the evaluation protocols, our full VFR consistently outperforms all the baselines and variants. Furthermore, since our VFR based on the previous work & [8], all these variants maintain a portion of its virtual try-on capability, achieving
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User
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Figure 8: In the virtual try-on with 360 dynamic motion, (a) our VFR generates high quality, long virtual try-on videos, while (b) removing either anchor video or prefix conditioning results in noticeable degradations. On the contrary, (c) the baselines suffer from smoothness and temporal consistency issues. Please refer to the project page for full streaming.
comparable GPT evaluation scores, where the D&D variant, as a training-fie free long video generation pipeline utilizing D&D, preserves the majority of its capability.
We also observe that our NP variant performs similar to our full model, reflecting the strong control provided by the anchor video. However, without the anchor video the NA and D&D variants suffer

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Method
Bubject Cousinency Bodliground Consistency Motinn Smoothnew GPTpya GGToe
GPTaze
GFT
GPTbund
1. Gurment Concitency (5s)
3MI CarmentCrrmnt Comistency (Mih)
Ours
14.06
96.23
99,37
98,099
RK.11
64.08
86.31
$7.14
Ours
NP
M1.58
96, 10
99.31
89.06
87.24
64.20
85.56
56.69
Our
NA
92.77
95.53
99.22
88.47
64.91
81.75
81.66
$4.04
Ours
D&D
93.70
86.16
Our
NR
90.90
99.31
85.55
Hand-Body Interaction Faillfuluess (98a)
Ous
92.13
94,02
99.24
87.20
84.85
77.85
81.12
$2.62
Ours
NP
91.88
93.91
99.15
80.65
66.25
80.15
63.22
$4.62
Oun
NA
88.00
91.50
99,29
85:20
80.55
70.67
69.05
75.70
Oun
DAD
91.65
91:25
99.11
89.98
87.25
82.53
88.15
86.15
NR
96,02
89,84
84.83
4-Med. Capobility for Arbitrary
(25g)
Our
91.09
M0.82
W8.62
81:11
84.45
71.83
79.77
$1.85
Our
NP
90. 19
91.00
91.85
87.40
K5.48
75.40
80.55
$2.56
Ours
NA
88.46
92.10
97, 97.92
86.87
$4.39
77.92
77.56
$1.31
Oun
D&D
89.27
92.80
97.83
87.83
85.23
98.24
82.22
63.61
Ouss
NR
87.56
85.28
83.75
4-Hord. Cagariilitt
Oun
03.21
85.33
Ours
NP
92.71
94.63
99.20
87.60
86.38
71.12
79.47
79.60
Our
NA
91:17
91.33 51
00.12
87.11
85.01
71.42
77.11
78.99
Ours
D&D
92.27
94,099
99.25
85.05
$4.97
71,90
81.38
$0.39
Our
NK
89.17
92.05
99.19
85.56
84.61
73.46
IVTv
D&D FramePack
88.33
90.98
85.55
Kling
Table 1: Quantitative experiments show that our VFR consistently outperforms all variants and
baselines in both consistency and smoothness metrics from VBench [88], while achieving comparable try-on and visual quality as Dress&Dance (D&D) [8]. Notations: NP is No Prefix; NA is No
Anchor, and NR is No Refine.
significant drop in both (human) subject and background consistencies, and the degradation of virtual try-on quality even occurs for the NA variant, as reflected in the GPT evaluation metric. This shows that the anchor video controls not only the consistent appearance but also the try-on quality.
Finally, we compare the most powerful baselines, FramePack [14] and Kling [86], combined with the state-of-the-ard virtual try-on method Press&Dancee FramePack has a significantly lower consistency metric, indicating that its conditioning method, which takes into account the previous frames, is not sufficient to enforce consistency. Kling still achieves a slightly lower consistency metric compared to our VFR, but the quality of the virtual try-on degrades.
Conclusion
We propose VFR, virtual try-on method that generates arbitrarily long, high-resolution videos from a single user image, garment, and motion reference. The key of our method is an anchor video-guides framework that ensures temporal consistency across segments and implicitly captures 3D structure, enabling free-viewpoint rendering without 3D supervision. Along with the prefix conditioning, we achieve both local smoothness and global temporal consistency in the long video generation. We further introduce a new evaluation protocol tailored to long video virtual try-on, covering garment fidelity, user appearance, and motion robustness. Experiments show that VFR produces realistic, smooth, temporally consistent, and garment- faithful results that significantly surpass the capabilities of prior methods. We believe that VFR opens up new avenues for interactive, personalized virtual try-on experiences- wwhtthe in e-commerce, virtual social platforms, or creative content generation.
Discussions. We made progress in generating arbitrarily long, high-resolution videos for virtual try-on. Our preliminary analysis shows that additional video data can help us further improve the quality of the generated videos. Secondly, while this work is the first of its kind, it takes 1~2 hours to generate a 30s video, which is not efficient enough. We leave this speed-up as an interesting future work. Finally, we believe that our work will pave the way for the transition from long videos to arbitrary 4D content, where user can both change camera perspective and motion.
Potential Societal Impacts. The positive societal impacts of our VFR may include (1) revolutionizing the online shopping experience for clothing, (2) decreasing returns and replacements of clothes through improved pre-sale understanding, and (3) leading volume of online clothing shops. On the other hand, VER is inherently a model that produces human videos, and also brings the risks produce biased, unethical, unsafe results.
increase in both the number and
Acknowledgement: We thank Pixophilia Technologies Private Limited, India, and ToorsechTools Co., LTD, Vietnam, for generously providing the visual data for our benchmark.
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NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification: Yes. As claimed in Sec. 1, we solved and achieved State-of-the-asd in long video virtual try-on.
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made in the paper.
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Declaration LLM usage
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Supplementary Material
A
Supplementary Video (sv)
We provide supplementary video (SV) as this YouTube video to visualize videos and comparisons against baselines. Here is table of contents of the SV:
SV0:17 360 Garment Consistency (Anchor Videos).
SV0:28 360 Human+Garment Consistency (30s).
SV1:27 Hand-Body Interaction Faithfulness (90s).
SV2:57 Capability for Arbitrary Poses Medium (25s).
SV3:23 Capability for Arbitrary Poses Hard (30-50s).
SV4:56 Comparison with VFR Variants (Ablation Study).
SV6:49 Comparison with Baselines (speeded-up to the same duration).
SV7:23 3D Reconstruction.
B
Implementation Details
B.1 Base Model and Architecture
Our VFR is built on Dress&Dance (D&D) [8], prior work that generates high-resolution, 5-second virtual try-on videos.
D&D proposes CondNets for unified conditioning. Our VFR introduces two different conditions: an anchor video and a prefix video. These conditions are passed to the diffusion transformer (DiT) [93] model with two additional CondNets.
B.2 Issues in CondNets
While the conditions in conventional D&D are either images or pixel-aligned videos (motion or refinement source). the conditions in VFR include the anchor video (pixel-mnaligned videos) and the prefix video (pixel-aligned, but with a shorter duration). These conditions are significantly more challenging for conditioning when generating each frame, the generator must consider the entire condition video instead of only the corresponding frame.
We observe that CondNets continue perform well for the anchor video in out-of-the-bot manner. However, when the prefix video is present, it is common for the continued video to exhibit artifacts, such floating dust, as demonstrated in the "No Refine" variant in SV4:56. This necessitates the of the immediate refiner to remove such artifacts.
B.3 Training Procedure
We train our VFR on 8x NVIDIA H100 GPUs using traditional PyTorch Distributed Data Parallel (DDP) framework. As mentioned in the "Experiments" " section the main paper, both the main generative model and the refiner are trained for 10,000 iterations both Internet and captured datasets from D&D [8].
In order to learn how to remove the specific dusty artifacts caused by the prefix conditions in the refiner, we employ new data augmentation method. We generate number of ovals with random sizes, positions, orientations, time intervals, and velocities, and then manually create artifacts in these regions by either increasing or decreasing brightness, or replacing them with other regions to create residual artifacts. With this procedure, our immediate refiner is capable of effectively removing various and aggressive artifacts which can be referred as the artifacts the "No Refine" variant's results in SV4:56.
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B.4 Generation Setting
The total number of frames F generated by VFR is 41, at rate of frames per second, resulting in a 5s video. In the generation of non-first segments, we select f 7- 9, which corresponds to a Is overlap between the current segment and the previous segment. Therefore, after generating the first segment of =F=== = 41 frames (5s), we produce F --f ff The cutting points of the video are at the (4k + 5) second, i.e., 0:05, 0:09, 0:13, 0:17, etc. Without prefix condition, i.e., the "No Prefix (NP)" variant, these positions most likely to have sudden changes or morphings.
32 new frames (4s) at each of the following segments.
C Additional Experimental Results
C.1 Additional Image Try-On Model for Baselines
We primarily use Dress& [8] image try-on as part of the baseline model for our experiments. Additionally, we also employ Kling try-on [86] and CatVTON [40] to establish some other baselines. As shown in SV6:49, although these methods generate reasonable virtual try-on images, the limitation of the animation models (FramePack or Kling in "repeat I2V" mode) still prevents them from achieving temporal consistency.
C.2 Ablation Study: Qualitative Experiments
The qualitative ablation studies are presented in SV4:56. From the video, we can clearly observe that the "No Prefix (NP)" variants exhibit highly noticeable sudden changes during the cut, highlighting the necessity of the prefix conditioning. Furthermore, for the "No Anchor (NA)" and "D&D" variants, the performance even degrades over time, emphasizing the importance of the anchor videos to stabilize the overall appearance and quality. Finally, without the immediate refiner, the NR variants show noticeable artifacts that accumulate an effective tool to prevent artifact accumulation and ensure robust long video generation. In practice, adopt fixed two-step lightweight refinement strategy, where each refinement round takes approximately 15-20 minutes on a single GPU, resulting in a total refinement time of about 30-40 minutes roughly 25% of the overall generation time, with the main generation requiring around 80-90 minutes. Overall, the "No Refine (NR)" variant exhibits mild background artifacts and boundary noise toward the end of long sequences while maintaining strong identity and garment consistency; the refinement effectively mitigates these degradations and enhances the temporal stability of the generated videos.
time. This demonstrates that the immediate refiner
C.3 Limitations of Metrics
As shown in SV4:56, there are indeed notable smoothness and consistency issues present in the variants and baselines, even for the most powerful variant NP. However, these metrics which report very similar numbers do not adequately reflect such discrepancies. Additionally, the GPT metric, which evaluates only the try-on quality, are also not effectively assessing our long virtual try-on generation quality, and they do not capture the consistency of the garments.
D 3D Reconstruction
As mentioned in the main paper, the anchor videos generated by VFR are almost 3D consistent, and therefore can be reconstructed into 3D shapes. We perform some of this reconstructions. As shown in SV7:23, even if the videos are not perfectly 3D consistent, we can still obtain a noisy yet reasonable 3D shape. Therefore, with advanced DD-to-DD distillation techniques like SDS [94], one might be able to actually obtain a precised a mesh and use it for rendering and deformation which implicitly achieved within our VFR.
Notably, being perfectly 3D consistent is not requirement of VFR. fact, the "Methodology" section of the main paper, VFR is trained to be sufficiently robust to support arbitrary video as the anchor video. It is also frequently the case that the generated anchor video has a stationary background while the person is rotating (SV0:17) - this kind of video also works well and is even
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more desirable, as the background is an integral part of the anchor that needs to remain consistent throughout the video. The 3D reconstruction results in SV7:23 are constructed from the video after background removal.
24


Reviews:
[{"review_id": "Wzli8tFffg", "reviewer": "Reviewer_VQN7", "summary": "This 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.", "questions": "Please refer to weaknesses.", "limitations": "Most limitations have been declared by the authors.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\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", "was_revised": true, "final_justification": "Thanks for authors' detailed response. My concerns are addressed. This work does contribute to the specific field of virtual try-on, but it does not contribute much to general video generation. I will raise my score.", "rebuttal": "> 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.\n\n- First, our *overall technical contribution* is not merely an “AR video generation way.” VFR takes a single image of the user and the garment to produce a **high-resolution, smooth, and temporally consistent long try-on video** where the user performs the desired **motion**. We devise *novel training schemes* that allow our model to be trained using **only short video data**.  \n  - This is fundamentally different from StarGen, which focuses on generating videos of a **static** scene.  \n- LCT extends full attention mechanisms from individual shots to the scene level by incorporating interleaved 3D positional embeddings and an asynchronous noise strategy.  \n  - In contrast, our VFR adopts a *standard and streamlined* conditional diffusion framework with multiple conditioning signals. While VFR currently does not use KV-caching, it could potentially benefit from a similar acceleration strategy (e.g., enabling KV-cache for prefix videos), which we consider a promising direction for future work. \n  - We also note that LCT is a concurrent work and has not been published in any peer-reviewed venue.  \n\n- Lastly, neither StarGen nor LCT addresses key challenges such as *over-time performance degradation*, *temporal inconsistency*, and *local smoothness*, as can be observed from the videos available on their respective websites. In contrast, our VFR is specifically designed to mitigate these issues, highlighting its **distinct and effective technical contributions**.  \n- We will clarify these points further in the final revision.  \n\n> 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.\n\n- During inference, the anchor video is generated as a **short (5s), low-FPS** video try-on task using our own method (L158), performed via a **single-step**, non-AR diffusion generation. This avoids the temporal inconsistency that only arises in *autoregressive* generation.  \n- In training, the model is explicitly optimized to “produce videos that are consistent with the (arbitrarily) given anchor video” (L197). More specifically, because the two sampled clips in training may differ substantially due to occlusions and motion dynamics, the model naturally learns to handle diverse anchor videos, including the A-pose anchor used at inference time.  \n\n> 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- The refinement process plays a key role in preventing performance degradation, as demonstrated in our ablation study (Suppl. L77, Suppl. Tab.C.1 “Ours NR”, SV4:56).  \n- As the refinement process operates on an **already generated video** and does not need to generate from scratch, it is substantially easier than the original generation. This allows us to distill it into an efficient one-step denoising model with comparable performance. We leave this optimization and the end-to-end approach as interesting directions for future work."}, {"review_id": "txeOWltv87", "reviewer": "Reviewer_RytG", "summary": "This 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.", "questions": "Please 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.", "limitations": "Yes.", "rating": 2, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**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.", "was_revised": true, "final_justification": "The reviewer appreciate the effort of author rebuttal. However, the rebuttal does not provide persuasive quantitative results on public benchmarks, so I will maintain my original rating as clear rejection.", "rebuttal": "> 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\n- We clarify that Table C.1 in our supplementary material presents quantitative comparisons against two baselines: **D\\&D+FramePack** and **D\\&D+Kling**.  \n- As the benchmark is large, the evaluation of these baselines is resource-intensive. Thus, we focused on the most representative and competitive baselines (e.g., **Kling**, a commercial product trained on extensive internal data).  \n- As there are currently no existing methods for *long video virtual try-on*, these composed baselines and our ablation variants represent the only viable points of comparison.  \n- We will move Table C.1 to the main paper and include additional baselines in our revision.  \n\n> 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\n- We kindly remind the reviewer that **our most powerful baselines**—D\\&D+FramePack and D\\&D+Kling—use the **same** Dress\\&Dance (D\\&D) dataset as our VFR model. This demonstrates that the private data *alone* is not sufficient to produce high-quality long try-on videos.  \n- Public video virtual try-on datasets, such as **VVT** (256×192) and **ViViD** (832×624, highly compressed), lack the resolution and fidelity needed for generating minute-scale outputs with detailed garment appearance. As such, the D\\&D dataset (≥1080p) is the only viable option for our high-resolution task.  \n- The inaccessibility of the anonymous GitHub link is caused by a known bug in the `tdurieux/anonymous_github` repository (see GitHub issues \\#395, \\#404), and is unrelated to our submission. \n  - As a reliable workaround, we kindly ask the reviewer to use the **“Download Repository”** button on D\\&D’s anonymous GitHub page to bypass the bug and access the full codebase, paper, and videos.\n\n> 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 weakness is also related to the second one, where the authors fail to elaborate the details of the 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\n- Since the anchor video is a short, low-FPS sequence, its generation does *not* require conditioning on another anchor video or a prefix. Instead, during inference, the anchor video is produced using our VFR via a single-step, non-autoregressive diffusion process. This avoids temporal inconsistency (which only happens in autoregressive generation) and does not rely on prefix conditioning.  \n- To support anchor video generation during training, we randomly drop both the anchor video and prefix conditions with a certain probability. This simulates the setting of anchor video generation. The dropout is handled by the **CondNet** conditioning mechanism (D\\&D Sec. 3.1), which passes an empty sequence of tokens to the transformer blocks when conditions are absent.\n  - Please refer to the D\\&D paper, included in the repository accessible via the **“Download Repository”** button.\n\n\n> I 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\n- We emphasize that our performance is not attributable to the dataset itself, as both **D\\&D+FramePack** and **D\\&D+Kling**—trained on the same data—fall short in video quality and consistency. Our ablation studies further highlight the contributions of our novel designs.  \n\n> The authors did not address the limitations and potential negative societal impact of their work.\n\n- We have outlined the limitations and potential societal impacts of our method in **L288–L299**, which have also been *acknowledged by other reviewers*."}, {"review_id": "pAid3qAFDT", "reviewer": "Reviewer_BXqh", "summary": "This 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.", "questions": "1. 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?", "limitations": "Yes", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strength：\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.", "was_revised": true, "final_justification": "I appreciate the authors’ detailed response. After carefully reviewing all the clarifications, I have decided to maintain my original score. My main concerns remain as follows:\n\nFirst, the paper does not provide quantitative comparisons against other state-of-the-art methods, and the qualitative evaluation is limited to a single video sequence. This makes it difficult to convincingly demonstrate the advantages and significance of the proposed approach.\n\nSecond, several important details remain unclear. For instance, it is not well explained how the anchor video is generated (e.g., if SMPL is not used, what alternative conditioning is applied, such as DWPose?). The authors referred me to the Dress&Dance [8] work for further clarification, but this reference points to an anonymous under-review paper. Moreover, the provided link appears to be broken, and I was unable to access the PDF, which makes it difficult to obtain the necessary information, \\eg, the CondNets, the dataset they used.", "rebuttal": "> 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.\n\n- We clarify that we do *not* use any underlying 3D human model such as SMPL. The anchor video is a generated virtual try-on sequence at a fixed A-pose and under a rotating camera trajectory (L158), rather than being based on any explicit 3D mesh or rigged model.  \n- Although the anchor video itself is relatively static, our final generated videos are capable of producing reasonable garment-specific dynamics. Examples include the flow of the black skirt and the checkered skirt (SV3:25), as well as the movement of the yellow trousers (SV4:14).  \n\n> 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.\n\n- During inference, the anchor video is generated using our VFR model, conditioned on the garment image, user image, motion prompt, and A-pose template. Since the anchor video is a short (5s), low-FPS video, it is produced via a **non-autoregressive** diffusion process. This avoids the inconsistency often observed in AR generation and does *not* require conditioning on another prefix or anchor video.  \n\n> 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.\n\n- We refer the reviewer to our prior work **Dress\\&Dance [8]** (Sec. 3) for details on how a target user and a specified garment are fused. This is achieved using *CondNets* (Sec. 3.1) for multi-modal conditioning, and a *garment warm-up training* phase (Sec. 3.2) to enhance garment-user fusion capability. Our method adopts this architecture with minimal modifications.  \n- When accessing the corresponding repository for [8], please use the **“Download Repository”** button to obtain the full codebase, instead of downloading individual files, to avoid the bugs of anonymous GitHub.  \n\n> 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\n- The first frame does not require separate generation. The initial prefix video is generated using the same VFR model without providing a prefix condition (L135). There is no separate model involved—our entire generation pipeline, including the generation of the first segment and the anchor video, uses a single VFR model with variable conditioning inputs.   \n\n> 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.  \n> During the generation process, how much time is spent on the VFR module versus the refiner?  \n\n- The necessity of immediate refinement is demonstrated in our ablation study (Suppl. L77, Suppl. Tab. C.1 “Ours NR”, SV4:56), where removing refinement leads to notable degradation over long videos.  \n- Our refiner is fine-tuned from a partially trained VFR and takes both the prefix and anchor video as input. Therefore, it would not be fair to apply the same refiner to baselines, which lack these conditioning signals. Without these two conditions, the refinement could mitigate some smoothness issues in the baselines, but the fundamental inconsistency would likely persist.  \n- Our generation uses a fixed **2-step refinement** strategy. Since refinement operates on an already generated video, it requires fewer denoising steps than the initial generation. On a single GPU, refinement takes approximately **30–40 minutes**, which is about **one-third the time** required for the main video generation. Altogether, refinement accounts for roughly **one-quarter of the total generation time** (~2 hours; see L290)."}, {"review_id": "IwMKLu1x5L", "reviewer": "Reviewer_6KVB", "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.", "questions": "None.", "limitations": "yes.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n1. The paper is well written and easy to follow.\n2. The idea of using an anchor video to improve consistency is effective and interesting.\n\nWeaknesses:\n1. The technical novelty of the proposed method appears limited, as it primarily combines existing techniques.\n2. 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.", "was_revised": true, "final_justification": "My concerns are addressed during the rebuttal phase. Thus I am raising my score to positive.", "rebuttal": "> The technical novelty of the proposed method appears limited, as it primarily combines existing techniques.\n\n- We respectfully disagree with this assessment.  \n  - Our VFR is, to our knowledge, the first framework designed specifically for **long video virtual try-on**. Prior work on video try-on (L83, L90; e.g., D\\&D, ViViD, Fashion-VDM) focuses on **short clips**, while prior work on long video generation addresses **non-try-on** domains (L99), such as text/image-to-video generation (e.g., FramePack) or static scene rendering (e.g., StarGen, CVPR’25).  \n  - Our core contributions include the **anchor video mechanism** and the **training scheme for temporal consistency over long durations with short video data**—both of which, to our knowledge, are novel in the context of both try-on and (long) video generation.  \n  - Notably, prior long-video methods (e.g., FramePack, StarGen, LCT) emphasize local smoothness but do not address **global temporal consistency** (L104), which is explicitly tackled by our anchor video design, tailored for the try-on setting.  \n- We further highlight that all existing SOTA methods, including strong commercial systems trained on extensive internal data (e.g., **Kling**), fail to produce high-quality long try-on videos without inconsistency or degradation. Our method is, to our knowledge, the first to satisfy all key requirements in this space.  \n- We would welcome clarification on which “existing techniques” the reviewer believes are merely being combined, so we may better address the concern.\n\n> 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.\n\n- We appreciate the opportunity to clarify this point. The term “**implicit 3D-aware generation**” in our paper (Fig. 4(b), the “cloud” portion) does *not* refer to the general notion of “3D-consistent video generation” or “3D world modeling” that some video generative models may exhibit to a limited extent.  \n- Specifically, **in our context**,  “implicit 3D-aware generation” refers to the following:  \n  - Our **anchor video**, as shown in Fig. 3(b), can be reconstructed into a 3D form and, thus, can be regarded as a **pseudo-3D representation** (L162).  \n  - Our generation process is *explicitly conditioned* on this pseudo-3D anchor video, enabling our model to preserve consistent user and garment appearance across time—what we refer to as *implicit 3D-awareness* (L164).  \n  - As other video generative models do *not* condition on such anchor videos, they do not support the “implicit 3D-aware generation” described in our work.  \n- More broadly, even leading commercial models do not reliably achieve 3D or temporal consistency, as illustrated by **Kling’s** performance in Fig. 6. In contrast, our method is the only one, to our knowledge, that enforces such consistency through **anchor-based conditioning**."}]

Author rebuttal:

Meta-review: This paper was reviewed by 4 experts in the field. The initial review is mostly leaning toward rejection. Still, after the authors clarified many concerns by the reviewers, two of the reviewers turned positive. The final rating to this work is mixed (rating: 2, 3, 4, 4).

Overall, this is a high-quality work solves an important problem. ACs think that the virtual try-on, problem that authors are trying to solve, is a very important to the community. Moreover, the list of challenges the authors was solving (garment consistency, human-garment interaction, and robustness to arbitrary poses, long-term generation) are important and long standing issues of this field.

Moreover, the visual quality is quite decent, as acknowledge by several reviewers.

Still, there are some issues being raised during the discussion. One concern is the technical contribution, as the proposed solution may be similar to other AR based video generation. During discussion, reviewers agreed that the proposed solution still has some novel design for Virtual Try-On tasks. Although this may limit its usage to other tasks, ACs feel that this still should be considered a novel framework.

Another concern is the quantitative evaluation, as the main paper does not have any quantitative evaluation. The only one is in supplementary material. Although this may limit the fidelity, for video generation, directly inspecting the visual result is still a valid way to evaluate in the community. Therefore, ACs did not think this issue itself is enough to reject this work.

The last one is reproducibility, as the authors are trained on private data. Although NeurIPS encourages release of all code and data, this is not a hard requirement. And evaluation shows in the current version is enough to demonstrate the effectiveness of this work.

Therefore, even though this work has several issues, ACs feel that this is still a high-quality work and would benefit the community. 

Considering all these, the decision is to recommend the paper for acceptance to NeurIPS 2025. Also, the authors are expected to release all codebase, trained model, and the proposed evaluation benchmark, as promised in the rebuttal.
Decision: Accept (poster)