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Kuldeep Kulkarni

8 accepted papers

2026

FlowCast: Trajectory Forecasting for Scalable Zero-Cost Speculative Flow Matching

ICLR 2026poster

Flow Matching (FM) has recently emerged as a powerful approach for high-quality visual generation. However, their prohibitively slow inference due to a large number of denoising steps limits their potential use in real-time or interactive applications. Existing acceleration methods, like distillatio…

Cited by 0SourceScholar
2026

Object-WIPER: Training-Free Object and Associated Effect Removal in Videos

CVPR 2026

In this paper, we introduce Object-WIPER, a training-free framework for removing dynamic objects and their associated visual effects from videos, and inpainting them with semantically consistent and temporally coherent content. Our approach leverages a pre-trained text-to-video diffusion transformer

Cited by 0SourceScholar
2025

Composing Parts for Expressive Object Generation

CVPR 2025poster

Image composition and generation are processes where the artists need control over various parts of the generated images. However, the current state-of-the-art generation models, like Stable Diffusion, cannot handle fine-grained part-level attributes in the text prompts. Specifically, when additiona…

Cited by 0SourcePDFScholar
2025

Imposter: Text and Frequency Guidance for Subject Driven Action Personalization using Diffusion Models

COLING 2025main

We present ImPoster, a novel algorithm for generating a target image of a ‘source’ subject performing a ‘driving’ action. The inputs to our algorithm are a single pair of a source image with the subject that we wish to edit and a driving image with a subject of an arbitrary class performing the driv…

2023

Blowing in the Wind: CycleNet for Human Cinemagraphs From Still Images

CVPR 2023poster

Cinemagraphs are short looping videos created by adding subtle motions to a static image. This kind of media is popular and engaging. However, automatic generation of cinemagraphs is an underexplored area and current solutions require tedious low-level manual authoring by artists. In this paper, we…

Cited by 16SourcePDFScholar
2021

SemIE: Semantically-Aware Image Extrapolation

ICCV 2021poster

We propose a semantically-aware novel paradigm to perform image extrapolation that enables the addition of new object instances. All previous methods are limited in their capability of extrapolation to merely extending the already existing objects in the image. However, our proposed approach focuses…

Cited by 16PDFScholar
2016

ReconNet: Non-Iterative Reconstruction of Images From Compressively Sensed Measurements

CVPR 2016poster

The goal of this paper is to present a non-iterative and more importantly an extremely fast algorithm to reconstruct images from compressively sensed (CS) random measurements. To this end, we propose a novel convolutional neural network (CNN) architecture which takes in CS measurements of an image…

Cited by 854PDFScholar