arXiv subjects

Snehashis Chakraborty

Publications and source records attributed to Snehashis Chakraborty.

2 recordsLinked to original sources

arXiv2026

A Two-step Metropolis Hastings Method for Bayesian Empirical Likelihood Computation with Application to Quantile Regression and Bayesian Model Selection

Empirical likelihood-based methods have been used under the Bayesian framework (BayesEL) in recent times. For statistical inference, these methods require efficient Markov chain Monte Carlo (MCMC) samplers for drawing observations from the parameter posterior distributions. However, the complex, especially non-convex, nature of the empirical likelihood support makes such MCMC algorithms harder to design. Such difficulties have restricted the use of BayesEL methods in many applications. In this article, we propose a two-step Metropolis-Hastings algorithm to sample from the BayesEL posteriors. Our proposal uses the current values of suitable subsets of the parameters and the estimating equations determining the underlying empirical likelihood to propose values of the remaining parameters. The proposed method is thus suitable for sampling from BayesEL posteriors in many complex problems, especially those with discontinuous estimating equations, e.g., simultaneous quantile regression. Furthermore, the proposed method easily extends to BayesEL model selection through a reversible jump Markov chain Monte Carlo procedure. Several illustrative, real-life applications of our proposed methods are presented.

stat.ME↗

arXiv2025

MuGa-VTON: Multi-Garment Virtual Try-On via Diffusion Transformers with Prompt Customization

Virtual try-on seeks to generate photorealistic images of individuals in desired garments, a task that must simultaneously preserve personal identity and garment fidelity for practical use in fashion retail and personalization. However, existing methods typically handle upper and lower garments separately, rely on heavy preprocessing, and often fail to preserve person-specific cues such as tattoos, accessories, and body shape-resulting in limited realism and flexibility. To this end, we introduce MuGa-VTON, a unified multi-garment diffusion framework that jointly models upper and lower garments together with person identity in a shared latent space. Specifically, we proposed three key modules: the Garment Representation Module (GRM) for capturing both garment semantics, the Person Representation Module (PRM) for encoding identity and pose cues, and the A-DiT fusion module, which integrates garment, person, and text-prompt features through a diffusion transformer. This architecture supports prompt-based customization, allowing fine-grained garment modifications with minimal user input. Extensive experiments on the VITON-HD and DressCode benchmarks demonstrate that MuGa-VTON outperforms existing methods in both qualitative and quantitative evaluations, producing high-fidelity, identity-preserving results suitable for real-world virtual try-on applications.

cs.CV↗