9 papers
Don't Settle at the Mode! Mitigating Diversity Collapse in Pretrained Flow Models via Feature Self-Guidance
Pradhaan S Bhat, Rishubh Parihar, Abhijnya Bhat +1
State-of-the-art flow models generate stunning images from text or image prompts. However, they suffer from diversity collapse when generating multiple samples under the same condi…
Keep The Essentials: Efficient Reference Conditioned Generation via Token Dropping
Rishubh Parihar, Ayush Raina, R. Venkatesh Babu +1
Reference-based diffusion models enable highly controllable image generation by leveraging elements from input images to guide prompt-driven synthesis. However, these models are co…
Thinking in Boxes: 3D Editing in Real Images Made Easy
Pradhaan S Bhat, Naveen Chandra R, Rishubh Parihar +4
Text and 2D-conditioning interfaces provide weak, ambiguous control over spatial transformations in image editing -- particularly under large object motions and camera changes. Pri…
SeeThrough3D: Occlusion Aware 3D Control in Text-to-Image Generation
Vaibhav Agrawal, Rishubh Parihar, Pradhaan Bhat +2
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-…
Kontinuous Kontext: Continuous Strength Control for Instruction-based Image Editing
Rishubh Parihar, Or Patashnik, Daniil Ostashev +3
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 cont…
MonoPlace3D: Learning 3D-Aware Object Placement for 3D Monocular Detection
Rishubh Parihar, Srinjay Sarkar, Sarthak Vora +2
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 gene…