Title: AnyFit: Controllable Virtual Try-on for Any Combination of Attire Across Any Scenario
Venue: NeurIPS 2024
Accepted: yes
Keywords: Virtual try-on

Diffusion models

Multiple conditions
Abstract: While image-based virtual try-on has made significant strides, emerging approaches still fall short of delivering high-fidelity and robust fitting images across various scenarios, as their models suffer from issues of ill-fitted garment styles and quality degrading during the training process, not to mention the lack of support for various combinations of attire. Therefore, we first propose a lightweight, scalable, operator known as Hydra Block for attire combinations. This is achieved through a parallel attention mechanism that facilitates the feature injection of multiple garments from conditionally encoded branches into the main network. Secondly, to significantly enhance the model's robustness and expressiveness in real-world scenarios, we evolve its potential across diverse settings by synthesizing the residuals of multiple models, as well as implementing a mask region boost strategy to overcome the instability caused by information leakage in existing models. 
Equipped with the above design, AnyFit surpasses all baselines on high-resolution benchmarks and real-world data by a large gap, excelling in producing well-fitting garments replete with photorealistic and rich details. Furthermore, AnyFit’s impressive performance on high-fidelity virtual try-ons in any scenario from any image, paves a new path for future research within the fashion community.

AnyFit: Controllable Virtual Try-on for Any Combination of Attire Across Any Scenario

Abstract

While image-based virtual try-on has made significant strides, emerging approaches still fall short of delivering high-fidelity and robust fitting images across various scenarios, as their models suffer from issues of ill-fitted garment styles and quality degrading during the training process, not to mention the lack of support for various combinations of attire. Therefore, we first propose a lightweight, scalable, operator known as Hydra Block for attire combinations. This is achieved through a parallel attention mechanism that facilitates the feature injection of multiple garments from conditionally encoded branches into the main network. Secondly, to significantly enhance the model's robustness and expressiveness in real-world scenarios, we evolve its potential across diverse settings by synthesizing the residuals of multiple models, as well as implementing a mask region boost strategy to overcome the instability caused by information leakage in existing models. Equipped with the above design, AnyFit surpasses all baselines on high-resolution benchmarks and real-world data by a large gap, excelling in producing well-fitting garments replete with photorealistic and rich details. Furthermore, AnyFit's impressive performance on high-fidelity virtual try-ons in any scenario from any image, paves a new path for future research within the fashion community.

1 Introduction

The dramatic success of e-commerce is steadily demanding a more convenient and personalized customer shopping experience. Among them, image-based virtual try-on (VTON) (23; 47; 22) has emerged as a promising topic in the research community and witnesses rapid advancements (15; 24; 18), whose task is to fit the target garment to the human body with various gestures. However, current methodologies do not meet the high-fidelity and robustness required in real-world applications, often resulting in artifacts or the mismatch of clothing details. Furthermore, the support for a variety of try-on combinations of attire (10) remains an area of ongoing research.

Most prior methodologies (6; 14) utilize a separate warping module to align garments on the human body, subsequently employing a Generative Adversarial Network (GAN) (19) for their integration. This explicit warping process typically yields overly smooth garment transformations and struggles to cope with complex poses and occlusions. (50). While some diffusion-based methods (58; 50) leverage pre-trained diffusion models (43), with structures akin to ReferenceNet (26) to preserve fine-grained garment information; However, these methods encounter difficulties in producing vivid fabric textures and photorealistic lighting and shadows. They also show artifacts in cross-category try-ons, diminishing their inherent text-image capacity when applied to specialized tasks as depicted in Fig. 4. In summary, existing methods still fall short in producing images of high fidelity that exhibit clothing styles rendered with exceptional detail and true-to-life accuracy across scenes. Moreover, these methods are designed solely for trying on individual items of clothing and do not support multi-conditions, thereby failing to facilitate the free combination of tops and bottoms.

As discussed above, we believe that an ideal VTON workflow should exhibit the following properties:

Scalability. The ultimate goal of the VTON model is to enable any free and customizable virtual outfit combination of multiple garments (57). It should support multi-condition injection, allowing for easy expansion to more applications, such as mixing and matching tops and bottoms, layering inner and outer garments, etc .

Robustness. Given the diverse scenarios encountered in e-commerce settings (13), the VTON model should generate authentic fabric textures and natural lighting, reproducing the details of the target clothing ( e.g. , logos, patterns, texts and strips) stably and accurately.

We present the following critical contributions to establish AnyFit as a novel VTON paradigm, which adeptly addresses the challenge of any combination of attire across any conceivable scenario, in Fig. 1. AnyFit mainly consists of two isomorphic U-Nets, namely HydraNet and MainNet. The former is tasked with extracting fine-grained clothing features, while the latter is responsible for generating try-ons. (1) Scalability : A hallmark of AnyFit is its innovative introduction of the Hydra Encoding Block that only parallelizes attention matrices within a sharing HydraNet, enabling effortless expansion to any quantity of conditions with only 8% increase in parameters for each additional branch. The proposal of parallelizing these blocks is built on the insight that only the self-attention layers are crucial for implicit warping (50), while the remaining components primarily serve as generic feature extractors. We further invent Hydra Fusion Block to seamlessly integrate the features of Hydra Encoding into MainNet, with positional embeddings to distinguish encodings from different sources. It is important to note that ReferenceNet (26; 9) or GarmentNet (50) could be seen as specific instances of HydraNet when limited to a single condition. (2) Robustness : Observations indicate a noticeable reduction in the robustness and quality of images generated by existing Virtual Try-On (VTON) works, in comparison to the original stable diffusion performances. Inspired by discussions in the community (2), we present the Prior Model Evolution strategy. This innovative approach involves merging parameter variations within a model family ( e.g. , a collection of fine-tuned versions of SDXL (41)), enabling the independent evolution of multiple capabilities of the base model. This strategy emerges as an intuitively logical and highly effective method for amplifying the model's innate potential prior to training. This is particularly relevant when contending with the significant escalation in training costs associated with dual U-Nets-an aspect that is overlooked in previous research. Furthermore, we introduce the Adaptive Mask Boost to further enhance the fit of the attire as a bonus. It requires length augmentation of parsing-free mask regions during the training phase, allowing the model to autonomously understand the overall shape of the clothing, which emancipates the model from previous reliance on hints of masks derived from garments. During inference, we adapt the shape of the mask area based on the aspect ratio of the target garment, thereby markedly encouraging the generation of well-fitted try-ons, particularly for long garments ( e.g. , windbreaker).

HydraNet

HydraEncodingBlock

"The model is wearing

Concatenate

[a blackT-shirt],pairedwith

*..

GN

[color-blocked longpants]"

+

Add

Cross-Attention

：

Hydra Fusion Block

Q

ND

Cross-Attention

K

IP-Adapter

Hydra Encoding Block !

V

Positon

Embedding

Cross-Attention

Attention

K

V

Q

IP-Adapter

Hydra Fusion Block

Pose

MainNet

Guider

To the best of our knowledge, AnyFit stands as a pioneering VTON method to fulfill scalability and robustness requirements. Our innovative HydraNet and Prior Model Evolution strategies have the potential to transform not just the domain of VTON, but to catalyze advancements across a broader spectrum of conditional generation applications. Finally, we have carried out comprehensive experiments on try-on benchmarks (12; 16) and engaged in challenging validation using in-the-wild sets. These experiments demonstrate that our model shows exceptional performance that eclipses current methods by a substantial margin, in terms of garment fidelity and robustness when addressing street-captured scenarios. In addition, our method has realized a formidable capability for multigarment try-ons, culminating in results that exhibit strikingly harmonized upper and lower styles.

2 Related works

GAN-based virtual try-on. The virtual try-on task is concerned with synthesizing images of a person donning the designated garment with appropriate fit (23), while retaining salient characteristics of the original garment and person, given a pair of images depicting a person and a target garment. To execute this task, numerous works (17; 30; 34; 6; 14; 22) have utilized Generative Adversarial Networks (GANs) (19) with two-stage strategy (35; 18; 52): (1) warping the clothing to the desired shape (7; 36) and (2) fusing the deformed clothing via try-on generator based on GAN. HR-VITON (35) conducts both warping and segmentation concurrently to address issues related to body occlusion and misfit of garments. GP-VTON (49) introduces localized warping along with global parsing to independently simulate the deformation of different regions of clothing, aiming to achieve a more form-fitting result. However, these existing approaches that rely on an explicit warping module are incapable of supplementing the sides of the clothing and the natural lighting and shadows (50).

Diffusion-based virtual try-on. As significant progress in Text-to-Image diffusion models (25; 39; 27; 11) is witnessed in recent years, some works (10) have been motivated to incorporate pre-trained diffusion models (43; 41) as priors into virtual try-on task. LADI-VTON (38) and DCI-VTON (20) explicitly deform the clothing to achieve pixel-level alignment with the human body, followed by a diffusion model to blend the clothing with the human body as refinement. StableVITON (31) introduces an end-to-end approach that injects intermediate feature maps from a spatial encoder into the U-Net decoder via a zero cross-attention block, akin to the ControlNet (55) structure. Most recently, OOTDiffusion (50) and IDM (13) achieve garment feature extraction with a parallel UNet and feed them through self-attention for enhanced integration. Unfortunately, these methods intrinsically lack support for try-ons that involve multiple garments. Moreover, they exhibit artifacts and unstable garment fits for arbitrary images, which leads to a degradation of performance on out-of-distribution images in complex backgrounds and poses.

3 Method

3.1 Model overview

An overview of the AnyFit is presented in Fig. 2. The backbone of AnyFit employs the SDXL (43), with the preliminary detailed in Appendix C.2. Given a human image x h ∈ R H × W × 3 and a target garment image x g ∈ R H × W × 3 , AnyFit is aimed to generate an authentic try-on image x tr . We employ OpenPose (8; 53) to obtain clothing-agnostic mask x m and masked person image x ag

adjusting for the size of different garments, as detailed in Sec. 3.3. We treat VTON as a specific case of image inpainting (51), endeavoring to fill the masked person x ag with the cloth x g . The main inpainting U-Net (MainNet) inputs 3 concatenated components with 9 channels: the noisy image z t , the latent agnostic image E ( x ag ) and the resized agnostic mask x m , where E ( · ) represents VAE (32) encoding. A Pose Guider (26) with 4 convolution layers ( 4 × 4 kernels, 2 × 2 strides, 16 , 32 , 64 , 128 channels) is incorporated to align the pose image E ( x p ) with noise z t .

Scalability: To preserve the fine details of the clothing, as well as to support both single and multiple garment VTONs, we employ a HydraNet that mirrors the MainNet in encoding clothing information. It shares the same weight initialization as the MainNet and innovatively parallelizes attention metrics based on the number of conditions to create Hydra Encoding Blocks for different conditional encodings. Robustness: During training, issues such as mask information leakage and quality degradation were observed. To address these issues, we adopt Adaptive Mask Boost and Prior Model Evolution, respectively, which significantly bolster the model's robustness across different scenarios cost-effectively and straightforwardly.

3.2 HydraNet for multi-condition VTON

HydraNet. Inspired by successful practice in human editing (26; 9), we introduce a garment encoding network isomorphic to the main generative network (MainNet), that precisely preserves the details of clothing. When dealing with multi-garment VTON, a direct method might involve replicating multiple garment encoding nets to manage different conditions. This approach, however, would lead to a significant increase in the number of parameters, rendering it computationally prohibitive. Experimentally we discover that for conditions with similar content (such as different types of clothing), the self-attention module plays a vital role in the latent warping of the garments, aligning them with the locations requiring inpainting. Conversely, other network architectures, which typically tasked with general feature extraction, can be shared across different condition encoding branches without compromising the model's performance. In view of this, we innovatively propose HydraNet for multi-condition encoding. It operates based on a shared Unet structure, while parallelizing the attention modules according to the number of input conditions, thereby constructing Hydra Encoding Blocks. Specifically, we parallelize the self-attention matrices with identical initial weights, and feed the multi-condition key and value features { z i hk , z i hv } into MainNet, which encode the fine-grained details of the clothing. It's notable that ReferenceNet (26; 9) or GarmentNet (50) could be seen as specific instances of HydraNet limited to a single condition. HydraNet requires only one forward pass (timestep t = 0 ) to encode clothing before the multiple denoising steps in MainNet, with the additional temporal and parameter overheads being minimal for each added condition, in Tab. 2.

Hydra Fusion. We propose a highly efficient and easily scalable Hydra Fusion Block to replace the self-attention layers in MainNet, accomplishing the feature injection from HydraNet to MainNet for any length via concatenation. Specifically, given the key and value features { z i hk , z i hv } ∈ R b × l × c from the HydraNet, we introduce learnable position embeddings to distinguish features from different source conditions. The superscript i denotes different input conditions. Subsequently, we concatenate the key and value along the l dimension to obtain the final { z all hk , z all hv } ∈ R b × Nl × c as:

where N denotes the total number of input conditions, PE represents positional encoding, and ⊕ signifies concatenation. z all hv follows a similar formulation. Facing the key and value features { z mk , z mv } ∈ R b × l × c from the MainNet and concatenated HydraNet features { z all hk , z all hv } , we once again concatenate the corresponding features along the l dimension into { z ck , z cv } ∈ R b × ( N +1) l × c , which are then used in subsequent attention calculations with z mq . It is noteworthy that, with the lightweight design leveraging parallelization and concatenation, HydraNet can be effortlessly extended to perform injections with any number of conditions, thereby possessing a more expansive application potential within the domain of generative models.

3.3 Model evolution and mask boost for robust VTON

Prior Model Evolution. A diminution in image generation performance with the SDXL-inpainting model compared to the SDXL base model is noted (1). We attribute this degradation to the disruption of previously well-aligned correspondences between text and images during the inpainting pretraining phase. Drawing inspiration from the open-source community (2), we develop a Prior Model

Garment

AnyFit(ours)

IDM

StableVITON

OOTDiffusion

HR-VTON

LADI-VTON

Evolution strategy, which enhances the model's strength and adaptability in generating outfit images at a very low cost, even without training. Specifically, we meticulously amalgamate the weights from three distinct, powerful models to evolve the initial weights for our model. These models comprise: SDXL-base-1.0 (41), SDXL-inpainting-0.1 (4) with inpainting capabilities, and DreamshaperXL alpha2 (3), which demonstrates superior performance in generating clothing and human figures. Their weights are denoted by W base , W inp , W ds . The evolution formula is as follows:

where α and β are the balancing coefficients that account for the capability enhancements from SDXL-inpainting and DreamshaperXL. It is important to note that we directly copy the extra 5 channels in the conv in layer of the SDXL-inpainting into the merged model, multiplying them by α .

However, the optimal values of α and β are not apparent. We hope to determine the optimal α and β to ensure that the initial weight W new achieve the best evaluation performance, i.e.

where Φ is a non-differentiable evaluation function. Empirically, we assume that f exhibits monotonic or convex properties with respect to the balancing coefficients ( α, β ) in most regions. Therefore, we discretize the continuous domain [0 , 2] 2 into a grid with δ = 0 . 1 as the step size and design the discrete greedy algorithm 1, to search for the optimal ( α, β ) . In our algorithm, we selecte the CLIP score (42) on 20 fixed inpainting image-text pairs as the evaluation function Φ . The optimal solution obtained is ( α, β ) = (1 . 0 , 1 . 1) . Please refer to the Appendix C.1 for more explanations.

Algorithm 1 Discrete greedy algorithm

Require:

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Adaptive Mask Boost. Previous works generally exhibit limited robustness in crosscategory try-on scenarios, resulting in inaccurately rendered clothing styles as shown in Fig. 6 and 10. This is largely due to a dependence on agnostic masks derived from clothing parsing, which tends to leak the edges of the clothing shape during training. This leakage may cause the generated garments to almost entirely cover the agnostic mask region. In response to these limitations, we employ an intuitive and effective approach that significantly enhances the model's robustness about cross-category try-on, i.e. , the Adaptive Mask Boost strategy, which primarily comprises mask augmentation during training and

adaptive elongation during inference. Specifically, during training, the agnostic mask is extracted solely using OpenPose body joint detections without leveraging human parsing . We perform random elongation of the mask by a factor f ∼ Uniform (1 . 2 , 1 . 5) with a probability of P = 0 . 5 . This training setting forces the model to autonomously determine the optimal cloth length. During inference, we assess the aspect ratio σ of the bounding box of the laid-out garment. If σ > 1 . 2 , we proportionally extend the agnostic area to match σ , creating an adaptive agnostic mask that conforms to the garment's style. Experiments have validated that AnyFit with Adaptive Mask Boost autonomously determines the appropriate garment length, yielding robust try-on results across different clothing categories.

(a) Visual comparisons on the proprietary dataset. AnyFit(proprietary) has capabilities for both open-garment and layered clothing. Even only trained on the VITON-HD, AnyFit has demonstrated excellent preservation of the fidelity of clothing.

Input

AnyFit(proprietary)

IDM

(2) Layering

capability

(3) Complex patterns

(1) Open-garment

rendering

StableVITON

OOTDiffusion

LADI-VTON

CLRE

W

4 Experiments

4.1 Experimental setup

Datasets. Our experiments are carried out on two publicly available datasets, VITON-HD (12) ( 11647 training pairs) and DressCode (16) ( 48392 training pairs) using the official splits for training and testing, as well as an additional proprietary e-commerce dataset. This proprietary dataset contains 50602 training pairs and 2500 testing pairs of mainly upper-body person and upper garment images, featuring complex patterns, backgrounds, and postures, alongside a rich variety of styles including layered garment ensembles, which present a more challenging scenario. As for multi-garment try-on, we utilized a HumanParsing model (28; 29) to extract clothing items from DressCode and constructed triplets consisting of (upper-body garment, lower-body garment, model image). In these triplets, one garment is an original laid-out image, while the other is a cropped image from the person's image. Finally, we construct 24314 publicly available upper-lower triplets by garment crop from the 15363 upper-body pairs and 8951 lower-body pairs in DressCode, called DressCode-multiple. A subset of 1800 triplets is reserved as the test set. Please refer to the Appendix B for more details.

Garment

AnyFit(ours)

IDM-2Stage

VTON-concat

Paint by example

Garment

AnyFit(ours)

IDM-2Stage

VTON-concat

Paint by example

(d) Aspect ratio σ sensitivity. Our model exhibits optimal performance at the appropriate σ . Additionally, as the mask aspect ratio σ continues to increase, the length of the try-on tends to remain constant, suggesting that our model potentially learns the correct length of the garments. Person Garment σ = 1.0 σ = 1.25 σ = 1.5 σ = 1.75 σ = 2 σ = 2.25

β

(c) Visual results about model evolution with various α, β after initialization without training. We discover that within a reasonable range, raising values of α and β yield improvements in the inpainting and outfitting capabilities.

Outfitting quality increasing

Inpainting ability increasing

α

= 1.0

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= 0.7

α

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(a) Comparison of performance from different initialization without training

Previous work

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Masked image

(b) Illustration about mask reliance found in previous work

Implementation details. We initialize the AnyFit with Prior Model Evolution strategy described in Sec. 3.3, and fine-tune it using an AdamW optimizer (37) with a constant learning rate of 5 e -5 . We train three variants of models on the VITON-HD, DressCode and proprietary dataset at a resolution of 1024 × 768 , independently. Subsequently, we extend the model trained on DressCode to enable multi-garment try-ons by training on the DressCode-multiple dataset. All the models are trained for 150 epochs on 8 NVIDIA A100 GPUs with DeepSpeed (5) ZeRO-2 to reduce memory usage, at a batch size of 15 . At inference time, we run AnyFit on a single NVIDIA RTX 3090 GPU for 30 sampling steps with the DDIM sampler (44). Outfitting dropout (50) is used with a guidance scale s g = 1 . 3 . The data augmentation follows the same protocol as in StableVTON (31). We also employ pretrained IP-Adapter (54) for SDXL. Please refer to the Appendix D for more details.

Del down  connection 0+1

Del up connection0

Del up connection1

Del up connection 0+1

Del cross-attn images

Del mid connection

Full model output

Network structure illustration of SDXL.

Orange lines represent self-attn connection.

Agnostic image

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Mid

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HM|服饰旗舰店

Connection

index

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Only ResBlock, No Attn

ResBlock+Attn

Evaluation protocols. We measure reconstruction accuracy by LPIPS (56) and SSIM (48) in a paired setting provided ground truth images, and authenticity of unpaired synthesized images by FID (40) and KID (45) without ground truth. All evaluations are conducted at a resolution of 512 × 384 .

Baselines. We compare our model on single try-on tasks on VITON-HD, DressCode and our proprietary dataset with previous baselines including HR-VTON (35), LADI-VTON (38), DCIVTON (20), StableVITON (31), OOTDiffusion (50), and the state of the art IDM (13). We directly utilize their released pre-trained models. As for multi-garment try-on, we developed a two-stage IDM model as a strong baseline, referred to as IDM-2Stage, which dresses the upper and lower garments sequentially. Inspired by (10), we concatenate the upper and lower garments on width spatially and feed them to a single-conditional HydraNet for training as another baseline, termed VTON-concat. Finally, we compared AnyFit with Paint by Example (51), IDM-2Stage, and VTON-concat.

Method

Paint-by-Example (51)

IDM-2Stage (13)

VTON-concat (10)

AnyFit (ours)

4.2 Qualitative results

Single-garment try-on. Fig.3 and 4 provide a qualitative comparison between AnyFit and the baselines on VITON-HD, the more challenging proprietary and in-the-wild data, covering opengarment and layering rendering scenarios. For a fair comparison with the baselines, we include results of AnyFit trained on VITON-HD. AnyFit excels in retaining intricate pattern details, owing to the effective collaboration between HydraNet and the IP-Adapter. It also maintains the correct silhouette of the clothing at a semantic level. This suggests that, through Mask Boost, AnyFit enhances the recollection of the original shape of the clothing, while other models, influenced by the mask, tend to generate incorrect appearances. The Prior Model Evolution further strengthens the texture representation of the apparel. Notably, when trained on the proprietary dataset, AnyFit automatically fills in inner garments or unzips clothing based on posture, a capability absent in the version trained on VITON-HD due to the lack of such training data.

Multi-garment try-on. Fig. 5 offers a qualitative comparison for multi-garment try-ons using the compiled DressCode-multiple dataset. Firstly, AnyFit demonstrates high-fidelity cloth preservation. Importantly, thanks to the distinct and individual Hydra-Blocks situated in different condition branches, AnyFit accurately depicts the demarcation between the upper and lower garments, showcasing a reasonable transition at the interconnection. In contrast, VTON-concat mishandles the relative clothing sizes after concatenation, leading to garment distortion and blurring. Meanwhile, IDM-2Stage faces artifacts at the juncture of the upper and lower garments, because it obscures parts of one garment while trying on another. Remarkably, despite training with one garment presented as a flat lay image and the other as a warped cloth cropped from a person image, AnyFit remains strikingly robust when faced with both garments presented as flat lays during inference.

4.3 Quantitative results

As indicated in Tab. 1 2 3, extensive experiments conducted on VITON-HD (12), DressCode (16), the proprietary dataset, and DressCode-multiple consistently prove that AnyFit significantly surpasses all baselines. This confirms AnyFit's capability to deliver superior try-on quality in both single-garment and multi-garment tasks across various scenes. Moreover, we note that AnyFit shows considerable improvement in unpaired settings in terms of the FID and KID metrics, demonstrating our model's robustness for cross-category try-ons. For more results, please refer to the Appendix E.

4.4 Ablation study

Hydra Blocks. To validate our proposed Hydra Blocks, we directly employ a singular conditioned HydraNet (which degenerates to ReferenceNet (26) actually) as the baseline "w/o Hydra Block" to encode both the top and bottom garment conditions concurrently, and then concatenate them into MainNet. As illustrated in Tab. 4, Fig. 8 and 11, a model lacking the Hydra Block tends to produce artifacts at the junction of the top and bottom garments. Such models also frequently allow the features of one garment to influence the other, leading to incorrect clothing styles. However, with the introduction of the Hydra Block, AnyFit consistently exhibits more stable results.

Prior Model Evolution. We qualitatively demonstrate the effects of the Prior Model Evolution in Fig. 13 and 6 (a). The SDXL-evolved model reduces artifacts and enhances robustness significantly, while outputs without Prior Model Evolution typically feature oversaturated colors as well as lighting and shadows that do not harmonize with the background. The gradual enhancement of model capabilities is visualized in Fig. 6(c). We also empirically and quantitatively validate the effectiveness

FID

↓

35.17

21.47

21.11

20.43

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13.12

7.85

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7.10

Time

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9%

(b) Visual comparison of ablation experiments on Hydra Blocks

Garment

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of the Prior Model Evolution strategy after training on the Virtual Try-On (VTON) task in Fig. 8 and Tab. 4. The Prior Model Evolution, by improving the model's initial capabilities, lessens the difficulty of learning and fosters a dramatic improvement in outfitting capacity and logo fidelity.

Adaptive Mask Boost. We illustratively showcase the issues of information leakage and mask reliance found in previous methods in Fig. 6 (b) and Fig. 10. Additionally, we empirically and quantitatively validate the effectiveness of the Adaptive Mask Boost strategy in Table 4 and Fig. 10. This strategy significantly heightens the model's robustness towards different categories of clothing, enabling the autonomous determination of appropriate garment length rather than relying on masks. Furthermore, we manually adjust the aspect ratios σ in Fig. 6 (d), which demonstrates the positive impact of adaptive elongation during inference. More ablation studies are detailed in the Appendix A.

5 Conclusion

We introduce AnyFit, a novel and robust VTON pipeline suitable for any combination of attire across any imaginable scenario, offering a revolutionary leap in realistic try-on effects. To support multigarment try-ons, AnyFit constructs HydraNet with lightweight and scalable parallelized attention that facilitates the feature injection of multiple garments. Observing artifacts in real-world scenarios, we evolve its potential by synthesizing the residuals of multiple models, as well as implementing a mask region boost strategy. Comprehensive experiments on high-resolution benchmarks and real-world data have demonstrated that AnyFit significantly surpasses all baselines by a large gap.

Broader impacts. With the ability to synthesize images, arises the risk that AnyFit might be used for inappropriate purposes such as producing media that breaches intellectual property rights or privacy norms. Because of these risks, we strongly advocate for the conscientious use of this technology.

Limitation and future work. Our approach exhibits excellent performance in single-garment and multi-garment virtual try-on applications. However, it still faces some limitations. Firstly, it shares the shortcomings of large text-image models, sometimes showing instability in generating hands with complex structures. Secondly, our model offers initial but not yet fully mature text control capabilities (for details, please refer to the Appendix A), providing opportunities for future enhancements.

References

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Discussions about model synthesis. https://www.reddit.com/r/StableDiffusion/ comments/zcby0o/you_can_now_merge_inpainting_and_regular_models/

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Appendix

In Section A, we provide more ablation studies about the role of text prompts in AnyFit, Hydra Block, Adaptive Mask Boost strategy and Prior Model Evolution. In Section B, we provide additional details about the proprietary dataset and the multi-garment VTON dataset. In Section C, we provide additional details about discrete greedy algorithm during Prior Model Evolution, and preliminary knowledge about stable diffusion. In Section D, we provide additional details about experimental setup, including data augmentation and outfitting dropout. Lastly, in Section E, we present a multitude of AnyFit generated images, including single-garment visual try-ons and multi-garment virtual try-ons, with additional results displayed in challenging scenarios.

A More ablation study

A.1 The role of text prompt in AnyFit

In fact, we have discovered that text plays a certain role in controlling the overall try-on style. As show in Fig. 9, by adjusting the prompt, AnyFit is able to achieve variations in Virtual Try-On (VTON) apparel styles. However, this form of control is unstable, leaving space for further exploration.

Garment

Original try-on

Sleeve up

With necklace

Open-garment

A.2 More ablation study about Adaptive Mask Boost strategy

The Adaptive Mask Boost strategy primarily comprises mask augmentation during training and adaptive elongation during inference. The role of adaptive elongation has been thoroughly illustrated in Fig. 6 in the main body, where it addresses the challenge of try-ons for long garments. Here, we predominantly discuss the function of parsing-free mask augmentation during training.

Specific to the implementation, during training, the agnostic mask is extracted solely using OpenPose body joint detections to occlude the original clothing, without leveraging human parsing, and random elongation of the mask is performed. Consequently, the shape of the mask is correlated only with the human pose and not with the clothing. The significance of this procedure lies in eliminating the possibility of the mask leaking clothing information, ensuring that the model cannot cheat from the shape of the mask . From Fig. 10, we observe that previous models often suffer from leakage of clothing information, where the models tend to generate garments that fill the entire mask areas. In real-world inference scenarios, it is challenging to provide an entirely accurate mask region.

Garment

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AnyFit(ours)

IDM

StableVITON

LADI-VTON

In contrast, our model is free from this predicament. Through mask augmentation during training, AnyFit autonomously determines the correct length of the garments, producing attractive try-on results.

A.3 More ablation study about Hydra Block

In Fig. 11, we present additional visual results to validate the effectiveness of the Hydra Block. "w/ Hydra Block" represents our fully proposed AnyFit, whereas the version "w/o Hydra Block" omits the Hydra Encoding Block as well as the positional embeddings within the Hydra Fusion. It directly

Block

Block

Garment Person

Block

Block

Garment Person

employs a singular conditioned HydraNet (which, in this scenario, degenerates to ReferenceNet) to encode both the top and bottom garment conditions concurrently, and then injects them into MainNet.

As illustrated in Fig. 11, model lacking the Hydra Block is more prone to producing artifacts at the junction of the top and bottom garments. They also tend to allow the features of the bottom garment to influence the top, resulting in incorrect clothing styles, such as overly long tops or erroneous stripe patterns. After equipped with the Hydra Block, AnyFit noticeably exhibits more stable results.

A.4 More ablation study about Prior Model Evolution

In the main body of the paper, we have demonstrated the effects of Prior Model Evolution. However, we also notice that the capabilities of large-scale text-to-image models are closely related to the value of their classifier-free guidance (CFG). For simplicity, in the main body of the paper, all images are generated using a CFG value of 3 . 0 in Fig. 6 when discussing the comparisons related to Prior Model Evolution. Here, we provide a more granular comparison concerning the presence or absence of the Prior Model Evolution strategy. The results presented here are all based on models directly initialized with synthetic weights, without any training.

As depicted in Fig. 13, an increase in the CFG value leads to a generalized oversaturation in the images. Within a reasonable range of CFG values, models incorporating Prior Model Evolution exhibit more realistic fabric textures and more plausible inpainting results. In contrast, outputs without Prior Model Evolution typically feature oversaturated colors and the absence of detailed wrinkles, as well as lighting and shadows that do not harmonize with the background. This illustrates the SDXL-inpainting-0.1 (41) model diminishes the exemplary text-to-image capabilities of the original SDXL-BASE model, resulting in more mediocre outcomes. We attribute this degradation to the disruption of the previously well-aligned correspondences between text and images during the inpainting pre-training phase. However, our model with Prior Model Evolution, significantly ameliorates this issue, enhancing its overall robustness. We actually find that after a minimal amount of fine-tuning, the model's synthetic outputs become substantially more powerful .

B Datasets

In this section, we provide a detailed description of the proprietary dataset and the multi-garment VTON dataset, i.e. , DressCode-multiple, which is constructed based on the publicly available DressCode (16) dataset.

B.1 The proprietary dataset

Our proprietary dataset comprises 50,602 training image pairs and 2,500 testing pairs. Each pair consists of a flat-laid garment image and a frontal upper body model image, with most model images featuring complex backgrounds. The proprietary dataset is collected from e-commerce websites, with attention paid to achieving a balanced distribution of clothing categories. We gathered apparel from 12 different categories, including both menswear and womenswear, across various seasons such as spring, summer, autumn, and winter. As illustrated in Fig. 12, the person images display diverse backgrounds. We standardized the image resolution to 1024x768 and conducted preprocessing on this basis to test different models. This preprocessing involved using Densepose (21) and OpenPose (8) to extract human features, employing the SAM (33) model to extract the main garment body, and constructing the gnostic mask images, etc .

LABZAA

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CFG  = 1.0

CFG  = 3.0

CFG  = 5.0

CFG  = 7.0

CFG  = 10.0

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Flat lay upper Cropped lower Person (a) Training data from upper-body data

lat lay

(c) Testing data

B.2 The DressCode-multiple dataset

To facilitate research on multi-garment virtual try-on, we require a dataset composed of image triplets, each containing an upper garment image, a lower garment image, and a model image wearing the corresponding garments. However, obtaining such data with strict alignment is challenging. Leveraging the DressCode dataset (16), which includes upper and lower garment data as well as full-body model images, we set out to construct the DressCode-multiple dataset, consisting of triplets, as illustrated in Fig. 14. Assuming we start with the upper garment data, where we already have a flat lay upper garment image and a model image wearing the corresponding upper garment, we use human parsing techniques (28; 29) to roughly segment the lower garment portion and extract it from the model image to serve as the corresponding lower garment image. At this point, the triplet consists of (flat lay upper, cropped lower, model image). Similarly, triplets derived from the lower garment data result in a composition of (cropped upper, flat lay lower, model image). Using this approach, we construct 24,314 publicly available upper-lower triplets by cropping garments from the 15,363 upper-body pairs and 8,951 lower-body pairs for training. For testing, we take the flat-laid garments from the upper and lower garment test sets, shuffle them, and combine them randomly, pairing them

with the test models from the upper garment data to create (flat lay upper, flat lay lower, model image) configurations for unpaired, real-world multi-garment virtual try-on tests. The paired testing is not conducted due to the lack of ground truth triplets.

It is worth mentioning that our model has not encountered triplets in the form of (flat lay upper garment, flat lay lower garment, model image) during training. However, it has already learned the correct way to wear upper and lower garments through training on cropped images and demonstrates good robustness. We believe that if real triplets consisting of (flat lay upper garment, flat lay lower garment, model image) were available for training, the model would exhibit even better performance.

C Method supplement

C.1 Discrete greedy algorithm

Our objective is to determine the optimal balancing coefficients α and β to ensure that the initial weight W new achieve the best evaluation performance, i.e.

Note that the function Φ is non-differentiable, which precludes the use of gradient-based optimization algorithms to find the minimum point. Empirically, we find that the evaluation function f exhibits monotonic or convex properties with respect to the balancing coefficients ( α, β ) in most regions. Therefore, we discretize the continuous domain [0 , 2] 2 into a grid with δ as the step size and design the following algorithm, inspired by the greedy method, to search for the optimal ( α, β ) .

In this algorithm, we initialize ( α, β ) at (0 . 5 , 0 . 5) . During each iteration, we calculate the evaluation value f at the current point and four adjacent points in the directions up, down, left, and right. The algorithm then updates the current point to the one with the minimum evaluation value among these adjacent points. The termination condition of the algorithm is met when the evaluation values of all neighbors are greater than that of the current point. This method can be viewed as a discrete version of gradient descent method. Please note that under the assumption that f is a convex or monotonic function, the algorithm is guaranteed to converge to the global optimal solution within the discrete parameter space.

C.2 Preliminary

Stable Diffusion. Our AnyFit is an extension of Stable Diffusion (43), which is one of the most commonly used latent diffusion models. Stable Diffusion employs a variational autoencoder (32) (VAE) that consists of an encoder E and a decoder D to enable image representations in the latent space. And a UNet ϵ θ is trained to denoise a Gaussian noise ϵ with a conditioning input encoded by a CLIP text encoder (42) τ θ . Given an image x and a text prompt y , the training of the denoising UNet ϵ θ is performed by minimizing the following loss function:

where t ∈ { 1 , ..., T } denotes the time step of the forward diffusion process, and z t is the encoded image E ( x ) with the added Gaussian noise ϵ ∼ N (0 , 1) ( i.e. , the noise latent). Note that the conditioning input τ θ ( y ) is correlated with the denoising UNet by the cross-attention mechanism (46).

D Experimental details

Data augmentation. We have implemented data augmentation techniques that could potentially enhance the model's generalization ability as well as its color accuracy performance. Specifically, the data augmentation operations include (a) horizontal flipping of images, (b) resizing garments and human figures through padding (up to 10% of the image size), (c) randomly adjusting the image's hue within a range of -5 to +5, and (d) randomly adjusting the image's contrast within a specified range (between 0.8 and 1.2 times the original contrast). Each of these operations occurs independently with a 50% probability. Moreover, these operations are simultaneously applied to both the garment and model images.

Outfitting dropout. Following the OOTDiffusion approach, we applied Outfitting Dropout, which essentially acts as a form of image-conditioned classifier-free guidance. It enhances the contrast and sharpness of the generated images. Specifically, during the training process of our MainNet, we randomly drop the input garment latent as E ( g ) = ∅ , where ∅ ∈ R 4 × h × w refers to an all-zero latent. In this way, the denoising UNet is trained both conditionally and unconditionally. Then at inference time, we simply use a guidance scale s g ≥ 1 to adjust the strength of conditional control over the predicted noise:

In practice, we empirically set the outfitting dropout ratio to 10% in training, and the guidance scale s g to 1 . 2 as default. We exclusively employ Outfitting Dropout within the Hydra Fusion Block that bridges HydraNet and MainNet. We have observed that applying outfitting dropout elsewhere introduces pixel-level artifacts.

E More experiment results

E.1 Full comparison on the proprietary dataset

Due to space constraints, we have not presented quantitative comparisons on the proprietary dataset in a paired setting within the main body of the paper; they are additionally reported in Tab. 5. Furthermore, we showcase additional results in Fig. 15, which include outcomes from state-of-the-art baselines and AnyFit trained on both the proprietary dataset and VITON-HD. Our method exhibits a considerable lead in performance.

E.2 More visual results

We provide more visual results on VITON-HD, DressCode, and proprietary dataset for inspection in Fig. 16, 17 and 18. Fig. 11 provides more results about multi-garment try-ons.

Input

AnyFit(proprietary)

IDM

DCI-VTON

StableVITON

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NOT·BAD

BA

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MA

07

VERSACE

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SEGCAACE

HEBSICE

EMINISM*

FEMINISM*

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WEEND

-tht

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Person

Output

Garment

Person

Output

RWB

RWE

Upper body

Dresses

Lower body

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nTo enhance the scalability (mix-and-match) and robustness (details clothing texture preservation) in virtual clothing try-on tasks, the paper proposed three contributions: i) design the Hydra Encoding Block to do parallel self-attention features transmition, ii) present the Prior Model Evolution to leverage groups of models' capabilities, and iii) introduce the Adaptive Mask Boost for generate garments structures without being affected by the original clothes on human.\n\nSTRENGTHS:\n- The paper designs the HydraNet to achieve multi-garment try-on with diffusion-based models. This is the first to extend the parallel UNet architecture towards multi-garment try-on.\n- The paper points to a critical challenge but few papers addressed this question, generating accurate torso length and sleeve length of target garments on humans. Also, the method shows decent performance to generate unknown textures when the target clothes are shorter than the original clothes on a human image.\n- The paper works decently on a complex background.\n\nWEAKNESSES:\n- The clearness of the presentation could be improved. For example, the usage of TryOnDiffusion [58] is cross-attention but the paper claims that [58] use self-attention (line #54). Also, it is unclear how to adjust clothing sizes (line #112), etc.\n- Fig. 6 (b) is not a new observation. The issue has been solved by the pre-processing method proposed by VITON-HD [12].\n\nQUESTIONS:\n1. Regarding line #54, there is inconsistency between the description in the paper and the original shared from TryOnDiffusion [58]. [58] uses cross-attention instead of self-attention, which is claimed to cause pixel-wise structural bias, to do implicit warping. Please clarify. On the other hand, OOTDiffusion [50] utilizes self-attention for preserving features. Can you share the pros and cons of leveraging cross-attention vs. self-attention for preserving features? And please state clearly the reason why this paper selected self-attention.\n2. Regarding line #68, it is unclear what “parsing-free mask region” is? Considering line #197, does it mean to exclude the third image counted from the left in left-bottom of Fig. 2 when training? What is the difference between the proposed Adaptive Mask Boost without using parsing to eliminate the contour of original clothes and the method used in HR-VITON, mentioned at the beginning of page 5 (Sec. 3: pre-processing)?\n3. I suggest including the notation mentioned in Sec. 3 in Fig. 2 to provide a clearer understanding.\n4. Does the Pose Guided mentioned in line #116 mean the Pose Encoder in Fig. 2?\n5. Why does the method train at the resolution of 1024x768 but evaluate at 512x384?\n6. Regarding the ablation study of Adaptive Mask Boost (AMB) in Table 4, the only matrix that evaluates the structure is SSIM but the result is worse when applying the Adaptive Mask Boost module. Can the paper describe more to prove that the table is good evidence to prove the effectiveness of AMB?\n7. Letting the model automatically find the suitable clothing length would misinterpret the accurate garment pattern, which is not expected in commercial usage. For example, we can only know that the model does not follow the original clothing length on input human to generate try-on results but we can’t know whether it generates accurate length, e.g., Fig. 9 and the sleeve length of the fourth case in the first row in Fig. 1. How does the paper think about designs that can ensure accuracy, e.g., COTTON [A]?\n8. It is unclear how the method adjusts the size that is mentioned in line #112 after reading Sec. 3.3. Does it mean automatically learned clothing size determination?\n9. Regarding line #133, how to prove that self-attention module instead of cross-attention module plays a vital role in the latent warping of the garments?\n10. Regarding the N parameter in Hydra Fusion, is it set to 2 when trying on top and bottom clothes? Can it extend to try on scarves, shoes, etc.?\n11. Fig. 12 lacks a direct visual comparison for proving the effectiveness of the proposed Prior Model Evolution. I suggest providing the figure with i) input human, ii) target clothes, and results from the four models, including SDXL-base-1.0, SDXL-inpainting-0.1, DreamshaperXL, and the merged one.\n12. How is the inference time for top and bottom try-on?\n\n[A] C. -Y. Chen, Y. -C. Chen, H. -H. Shuai and W. -H. Cheng, \"Size Does Matter: Size-aware Virtual Try-on via Clothing-oriented Transformation Try-on Network,\" *2023 IEEE/CVF International Conference on Computer Vision (ICCV)*, Paris, France, 2023, pp. 7479-7488, doi: 10.1109/ICCV51070.2023.00691.\n\nLIMITATIONS:\nThe authors have shared the limitations and potential societal impact.", "TITLE": "Official Review", "RECOMMENDATION": 7, "REVIEWER_CONFIDENCE": 5, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-07"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces AnyFit, a solution for multi-garment try-on. Specifically, they propose HydraNet to simultaneously extract features of both upper and lower garments. Then, they present Hydra Fusion to integrate these garment features into a denoising Unet. Additionally, they propose an Adaptive Mask, combined with text, to explore initial controllable capabilities.\n\nSTRENGTHS:\n1. They explore multi-garment virtual try-on, an area that has not been extensively covered in previous literature based on diffusion models.\n\n2. The paper is clearly written, and the related experiments are sufficient.\n\n3. They explored initially controllable try-on, which has been less investigated in prior literature.\n\nWEAKNESSES:\n1. There are no major theoretical and technical innovations. For example, HydraNet is a simple variant of the Unet copy which has been extensively adopted as the feature extractor in previous work such as AnimateAnyone and OOTDDiffusion.\n\n2. Similar to other try-on methods based on diffusion models, AnyFit relies on a large amount of high-quality images. However, their proprietary e-commerce dataset is not open, which does not further prompt the development of the try-on task.\n\nQUESTIONS:\n1. Why is there a qualitative comparison between AnyFit and OOTDiffusion in the paper, but no quantitative comparison? Additionally, the original paper of OOTDiffusion reported their quantitative results on VITON-HD and Dress Code at the resolution of 512×384.\n\n2. OutfitAnyone (https://github.com/HumanAIGC/OutfitAnyone, https://huggingface.co/spaces/HumanAIGC/OutfitAnyone) may serve as a good qualitative baseline for the multi-garment try-on task.\n\n3. Although it doesn't affect the conclusions, I'm curious why some baseline methods reported significantly different quantitative metrics between AnyFit and OOTDiffusion? For example, on Dresscode, AnyFit reported an FID of 6.94 for LADI-VTON, whereas OOTDiffusion reported an FID of 5.66.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-08"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a novel approach of controllable virtual try-on for any combination of attire across any scenario. It employs a Hydra Block, a lightweight and scalable operator designed for attire combinations, facilitated by a parallel attention mechanism that enables injecting multiple garment features into the main network. The model's robustness and expressiveness are enhanced by synthesizing multiple models' residuals and a mask region boost strategy, which addresses instability issues. The proposed method outperforms benchmarks on high-resolution images and real-world data, showing promising generated results.\n\nSTRENGTHS:\n1. Anyfit successfully mitigates the limitations of current virtual try-on methods, i.e., handling diverse attire combinations and scenarios. \n2. The novel Hydra Block and its parallel attention mechanism are well-explained, and the mask region boost strategy is theoretically sound. \n3. The experiments are comprehensive, with detailed reporting of the training and testing procedures, improving the reproducibility of the results. \n4. Overall the paper is well written, with clear explanations and logical structuring.\n\nWEAKNESSES:\nThe manuscript commendably provides a thorough ablation study with convincing results. However, the comparison based on SD1.5 is missing. Adding that leads to a more fair comparison with baselines that use SD1.5 backbones, such as LADI-VTON, StableVTON, and OOTDiffusion.\n\nQUESTIONS:\nAlthough the paper claims to combine any clothing items, the visualized results only show outfits with tops and bottoms. Can this method currently support combinations of more clothing items, such as shirts, jackets, and pants?\n\nLIMITATIONS:\nAs discussed in the paper by the authors, AnyFit sometimes shows instability in generating hands with complex structures and the text control capabilities of the method are not perfect yet.", "TITLE": "Official Review", "RECOMMENDATION": 7, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-10"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nCurrent image-based virtual try-on methods struggle with achieving high-fidelity and robust fitting across diverse scenarios due to issues like ill-fitting garments and quality degradation. To address this, AnyFit is introduced, leveraging a lightweight, scalable Hydra Block that facilitates feature injection of multiple garments through parallel attention. Additionally, AnyFit enhances robustness and expressiveness by synthesizing residuals from multiple models and implementing a mask region boost strategy. AnyFit outperforms baselines in high-resolution benchmarks and real-world data, enabling high-fidelity virtual try-ons in any scenario, paving the way for future fashion research.\n\nSTRENGTHS:\n1) AnyFit introduces the innovative Hydra Block, a lightweight and scalable operator that revolutionizes attire combinations through a parallel attention mechanism. This ensures seamless feature integration of multiple garments, overcoming issues of ill-fitting styles and quality degradation.\n\n2) By synthesizing residuals from multiple models and implementing a mask region boost strategy, AnyFit significantly enhances its robustness and expressiveness in real-world scenarios. This ensures stable and high-fidelity virtual try-on experiences across diverse settings.\n\nWEAKNESSES:\n1) While AnyFit offers advanced features and high-fidelity results, its complex parallel attention mechanism and multi-model residual synthesis may lead to increased computational requirements.\n\n2) The performance of AnyFit, like many image-based virtual try-on systems, relies heavily on accurate garment segmentation from the input image. In cases where the segmentation is imprecise or incomplete, the resulting virtual try-on may suffer from artifacts or incorrect fitting, affecting the overall realism and user experience.\n\n3) Training on two limited general datasets may not achieve the desired level of high generalization.\n\nQUESTIONS:\n1) How does AnyFit overcome issues with ill-fitted garment styles and quality degrading during the training process?\n\n2) Why are the baseline methods in Table 3 missing from Table 1?\n\n3) How does the proposed framework effectively prevent errors when the clothing-agnostic mask results in misestimations?\n\n4) What about the computational complexity of the proposed model?\n\nLIMITATIONS:\nAs the authors mentioned,\n1) The proposed model shares the shortcomings of large text-image models, sometimes showing instability in generating hands with complex structures. \n\n2) The proposed model offers initial but not yet fully mature text control capabilities.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-13"}