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Rishubh Parihar

8 accepted papers

2026

Kontinuous Kontext: Continuous Strength Control for Instruction-based Image Editing

Rishubh Parihar, Or Patashnik, Daniil Ostashev, Venkatesh Babu Radhakrishnan, Daniel Cohen-Or, Kuan-Chieh Jackson Wang
CVPR 2026

Instruction-based image editing offers a powerful and intuitive way to manipulate images through natural language. Yet, relying solely on text instructions limits fine-grained control over the extent of edits. We introduce Kontinuous Kontext, an instruction-driven editing model that provides a new d

Cited by 0Source ↗Scholar ↗
2026

SeeThrough3D: Occlusion Aware 3D Control in Text-to-Image Generation

Vaibhav Agrawal, Rishubh Parihar, Pradhaan S Bhat, Ravi Kiran Sarvadevabhatla, Venkatesh Babu Radhakrishnan
CVPR 2026

We identify occlusion reasoning as a fundamental yet overlooked aspect for 3D layout-conditioned generation. It is essential for synthesizing partially occluded objects with depth-consistent geometry and scale. While existing methods can generate realistic scenes that follow input layouts, they ofte

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2025

Compass Control: Multi Object Orientation Control for Text-to-Image Generation

Rishubh Parihar, Vaibhav Agrawal, Sachidanand VS, Venkatesh Babu Radhakrishnan
CVPR 2025poster

Existing approaches for controlling text-to-image diffusion models, while powerful, do not allow for explicit 3D object-centric control, such as precise control of object orientation. In this work, we address the problem of multi-object orientation control in text-to-image diffusion models. This ena…

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2025

MonoPlace3D: Learning 3D-Aware Object Placement for 3D Monocular Detection

Rishubh Parihar, Srinjay Sarkar, Sarthak Vora, Jogendra Nath Kundu, R. Venkatesh Babu
CVPR 2025poster

Current monocular 3D detectors are held back by the limited diversity and scale of real-world datasets. While data augmentation certainly helps, it's particularly difficult to generate realistic scene-aware augmented data for outdoor settings. Most current approaches to synthetic data generation foc…

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2024

Balancing Act: Distribution-Guided Debiasing in Diffusion Models

Rishubh Parihar, Abhijnya Bhat, Abhipsa Basu, Saswat Mallick, Jogendra Nath Kundu, R. Venkatesh Babu
CVPR 2024poster

Diffusion Models (DMs) have emerged as powerful generative models with unprecedented image generation capability. These models are widely used for data augmentation and creative applications. However DMs reflect the biases present in the training datasets. This is especially concerning in the contex…

Cited by 16Source ↗PDF ↗Scholar ↗
2023

Strata-NeRF : Neural Radiance Fields for Stratified Scenes

Ankit Dhiman, R Srinath, Harsh Rangwani, Rishubh Parihar, Lokesh R Boregowda, Srinath Sridhar +1
ICCV 2023poster

Neural Radiance Fields (NeRF) approaches learn the underlying 3D representation of a scene and generate photo-realistic novel views with high fidelity. However, most proposed settings concentrate on 3D modelling a single object or a single level of a scene. However, in the real world, a person captu…

Cited by 4PDF ↗Scholar ↗
2022

Hierarchical Semantic Regularization of Latent Spaces in StyleGANs

Tejan Karmali, Rishubh Parihar, Susmit Agrawal, Harsh Rangwani, Varun Jampani, Maneesh Singh +1
ECCV 2022poster

"Progress in GANs has enabled the generation of high-resolution photorealistic images of astonishing quality. StyleGANs allow for compelling attribute modification on such images via mathematical operations on the latent style vectors in the W/W+ space that effectively modulate the rich hierarchical…

Cited by 10Source ↗PDF ↗Scholar ↗