Debapriya Basu Roy

Debapriya Roy, Sanchayan Santra, Diganta Mukherjee et al.

The idea of \textit{Virtual Try-ON} (VTON) benefits e-retailing by giving an user the convenience of trying a clothing at the comfort of their home. In general, most of the existing VTON methods produce inconsistent results when a person posing with his arms folded i.e., bent or crossed, wants to try an outfit. The problem becomes severe in the case of long-sleeved outfits. As then, for crossed arm postures, overlap among different clothing parts might happen. The existing approaches, especially the warping-based methods employing \textit{Thin Plate Spline (TPS)} transform can not tackle such cases. To this end, we attempt a solution approach where the clothing from the source person is segmented into semantically meaningful parts and each part is warped independently to the shape of the person. To address the bending issue, we employ hand-crafted geometric features consistent with human body geometry for warping the source outfit. In addition, we propose two learning-based modules: a synthesizer network and a mask prediction network. All these together attempt to produce a photo-realistic, pose-robust VTON solution without requiring any paired training data. Comparison with some of the benchmark methods clearly establishes the effectiveness of the approach.

Debapriya Roy, Sanchayan Santra, Diganta Mukherjee et al.

The system of Virtual Try-ON (VTON) allows a user to try a product virtually. In general, a VTON system takes a clothing source and a person's image to predict the try-on output of the person in the given clothing. Although existing methods perform well for simple poses, in case of bent or crossed arms posture or when there is a significant difference between the alignment of the source clothing and the pose of the target person, these methods fail by generating inaccurate clothing deformations. In the VTON methods that employ Thin Plate Spline (TPS) based clothing transformations, this mainly occurs for two reasons - (1)~the second-order smoothness constraint of TPS that restricts the bending of the object plane. (2)~Overlaps among different clothing parts (e.g., sleeves and torso) can not be modeled by a single TPS transformation, as it assumes the clothing as a single planar object; therefore, disregards the independence of movement of different clothing parts. To this end, we make two major contributions. Concerning the bending limitations of TPS, we propose a human AnaTomy-Aware Geometric (ATAG) transformation. Regarding the overlap issue, we propose a part-based warping approach that divides the clothing into independently warpable parts to warp them separately and later combine them. Extensive analysis shows the efficacy of this approach.

Debapriya Roy, Diganta Mukherjee, Bhabatosh Chanda

With the increasing popularity of augmented and virtual reality, retailers are now focusing more towards customer satisfaction to increase the amount of sales. Although augmented reality is not a new concept but it has gained much needed attention over the past few years. Our present work is targeted towards this direction which may be used to enhance user experience in various virtual and augmented reality based applications. We propose a model to change skin tone of a person. Given any input image of a person or a group of persons with some value indicating the desired change of skin color towards fairness or darkness, this method can change the skin tone of the persons in the image. This is an unsupervised method and also unconstrained in terms of pose, illumination, number of persons in the image etc. The goal of this work is to reduce the time and effort which is generally required for changing the skin tone using existing applications (e.g., Photoshop) by professionals or novice. To establish the efficacy of this method we have compared our result with that of some popular photo editor and also with the result of some existing benchmark method related to human attribute manipulation. Rigorous experiments on different datasets show the effectiveness of this method in terms of synthesizing perceptually convincing outputs.

Debapriya Roy, Sanchayan Santra, Bhabatosh Chanda

In this paper, we propose a Landmark Guided Virtual Try-On (LGVTON) method for clothes, which aims to solve the problem of clothing trials on e-commerce websites. Given the images of two people: a person and a model, it generates a rendition of the person wearing the clothes of the model. This is useful considering the fact that on most e-commerce websites images of only clothes are not usually available. We follow a three-stage approach to achieve our objective. In the first stage, LGVTON warps the clothes of the model using a Thin-Plate Spline (TPS) based transformation to fit the person. Unlike previous TPS-based methods, we use the landmarks (of human and clothes) to compute the TPS transformation. This enables the warping to work independently of the complex patterns, such as stripes, florals, and textures, present on the clothes. However, this computed warp may not always be very precise. We, therefore, further refine it in the subsequent stages with the help of a mask generator (Stage 2) and an image synthesizer (Stage 3) modules. The mask generator improves the fit of the warped clothes, and the image synthesizer ensures a realistic output. To tackle the problem of lack of paired training data, we resort to a self-supervised training strategy. Here paired data refers to the image pair of model and person wearing the same cloth. We compare LGVTON with four existing methods on two popular fashion datasets namely MPV and DeepFashion using two performance measures, FID (Fréchet Inception Distance) and SSIM (Structural Similarity Index). The proposed method in most cases outperforms the state-of-the-art methods.

Manaar Alam, Arnab Bag, Debapriya Basu Roy et al.

Neural Networks (NN) have recently emerged as backbone of several sensitive applications like automobile, medical image, security, etc. NNs inherently offer Partial Fault Tolerance (PFT) in their architecture; however, the biased PFT of NNs can lead to severe consequences in applications like cryptography and security critical scenarios. In this paper, we propose a revised implementation which enhances the PFT property of NN significantly with detailed mathematical analysis. We evaluated the performance of revised NN considering both software and FPGA implementation for a cryptographic primitive like AES SBox. The results show that the PFT of NNs can be significantly increased with the proposed methodology.

Arnab Bag, Sikhar Patranabis, Debapriya Basu Roy et al.

FPGAs (Field Programmable Gate arrays) have gained massive popularity today as accelerators for a variety of workloads, including big data analytics, and parallel and distributed computing. This has fueled the study of mechanisms to provision FPGAs among multiple tenants as general purpose computing resources on the cloud. Such mechanisms offer new challenges, such as ensuring IP protection and bitstream confidentiality for mutually distrusting clients sharing the same FPGA. A direct adoption of existing IP protection techniques from the single tenancy setting do not completely address these challenges, and are also not scalable enough for practical deployment. In this paper, we propose a dedicated and scalable framework for secure multi-tenant FPGA provisioning that can be easily integrated into existing cloud-based infrastructures such as OpenStack. Our technique has constant resource/memory overhead irrespective of the number of tenants sharing a given FPGA, and is provably secure under well-studied cryptographic assumptions. A prototype implementation of our proposition on Xilinx Virtex-7 UltraScale FPGAs is presented to validate its overheads and scalability when supporting multiple tenants and workloads. To the best of our knowledge, this is the first FPGA provisioning framework to be prototyped that achieves a desirable balance between security and scalability in the multi-tenancy setting.