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20162026
most citedDenoising Monte Carlo Renders with Diffusion Models

1 citations · 2 across the 8 of their papers we have counts for

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cs.CV2026

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…

cs.CV2024

Improved Convex Decomposition with Ensembling and Negative Primitives

Vaibhav Vavilala, Florian Kluger, Seemandhar Jain +3

Describing a scene in terms of primitives -- geometrically simple shapes that offer a parsimonious but accurate abstraction of structure -- is an established and difficult fitting…

cs.CV2024★ 1 cited

Denoising Monte Carlo Renders with Diffusion Models

Vaibhav Vavilala, Rahul Vasanth, David Forsyth

Physically-based renderings contain Monte Carlo noise, with variance that increases as the number of rays per pixel decreases. This noise, while zero-mean for good modern renderers…

cs.CV2023

Convex Decomposition of Indoor Scenes

Vaibhav Vavilala, David Forsyth

We describe a method to parse a complex, cluttered indoor scene into primitives which offer a parsimonious abstraction of scene structure. Our primitives are simple convexes. Our m…

cs.CV2023★ 1 cited

Blocks2World: Controlling Realistic Scenes with Editable Primitives

Vaibhav Vavilala, Seemandhar Jain, Rahul Vasanth +2

We present Blocks2World, a novel method for 3D scene rendering and editing that leverages a two-step process: convex decomposition of images and conditioned synthesis. Our techniqu…

cs.CV2023

Dequantization and Color Transfer with Diffusion Models

Vaibhav Vavilala, Faaris Shaik, David Forsyth

We demonstrate an image dequantizing diffusion model that enables novel edits on natural images. We propose operating on quantized images because they offer easy abstraction for pa…