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20222026
most citedLatents2Segments: Disentangling the Latent Space of Generative Models for Semantic Segmentation of Face Images

1 citations · 1 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CV2026

ENet-GP: Unified Document Image Restoration

Sujal Burad, Aakanksha, A. N. Rajagopalan +1

Reliable document digitization in uncontrolled capture settings is challenging because real images exhibit multiple interacting degradations rather than a single isolated distortio…

cs.CV2026

Probability-Flow Distillation: Distribution Matching in Parameter Space

Rohith Ramanan, A. N. Rajagopalan

Score distillation methods use pretrained diffusion models as priors for optimizing parameters through differentiable forward models, most notably in text-to-3D generation. Yet the…

cs.CV2024

PNeRV: A Polynomial Neural Representation for Videos

Sonam Gupta, Snehal Singh Tomar, Grigorios G Chrysos +2

Extracting Implicit Neural Representations (INRs) on video data poses unique challenges due to the additional temporal dimension. In the context of videos, INRs have predominantly…

cs.CV2023

Latents2Semantics: Leveraging the Latent Space of Generative Models for Localized Style Manipulation of Face Images

Snehal Singh Tomar, A. N. Rajagopalan

With the metaverse slowly becoming a reality and given the rapid pace of developments toward the creation of digital humans, the need for a principled style editing pipeline for hu…

cs.CV2022★ 1 cited

Latents2Segments: Disentangling the Latent Space of Generative Models for Semantic Segmentation of Face Images

Snehal Singh Tomar, A. N. Rajagopalan

With the advent of an increasing number of Augmented and Virtual Reality applications that aim to perform meaningful and controlled style edits on images of human faces, the impetu…