Author name cluster

Pratul Srinivasan

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

1 author row

NeurIPS Conference 2025 Conference Paper

ROGR: Relightable 3D Objects using Generative Relighting

Jiapeng Tang
Matthew Levine
Dor Verbin
Stephan Garbin
Matthias Niessner
Ricardo Martin Brualla
Pratul Srinivasan
Philipp Henzler

We introduce ROGR, a novel approach that reconstructs a relightable 3D model of an object captured from multiple views, driven by a generative relighting model that simulates the effects of placing the object under novel environment illuminations. Our method samples the appearance of the object under multiple lighting environments, creating a dataset that is used to train a lighting-conditioned Neural Radiance Field (NeRF) that outputs the object's appearance under any input environmental lighting. The lighting-conditioned NeRF uses a novel dual-branch architecture to encode the general lighting effects and specularities separately. The optimized lighting-conditioned NeRF enables efficient feed-forward relighting under arbitrary environment maps without requiring per-illumination optimization or light transport simulation. We evaluate our approach on the established TensoIR and Stanford-ORB datasets, where it improves upon the state-of-the-art on most metrics, and showcase our approach on real-world object captures.

PDF Details

NeurIPS Conference 2024 Conference Paper

CAT3D: Create Anything in 3D with Multi-View Diffusion Models

Ruiqi Gao
Aleksander Hołyński
Philipp Henzler
Arthur Brussee
Ricardo Martin-Brualla
Pratul Srinivasan
Jonathan T. Barron
Ben Poole

Advances in 3D reconstruction have enabled high-quality 3D capture, but require a user to collect hundreds to thousands of images to create a 3D scene. We present CAT3D, a method for creating anything in 3D by simulating this real-world capture process with a multi-view diffusion model. Given any number of input images and a set of target novel viewpoints, our model generates highly consistent novel views of a scene. These generated views can be used as input to robust 3D reconstruction techniques to produce 3D representations that can be rendered from any viewpoint in real-time. CAT3D can create entire 3D scenes in as little as one minute, and outperforms existing methods for single image and few-view 3D scene creation.

PDF Details DOI

NeurIPS Conference 2020 Conference Paper

Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains

Matthew Tancik
Pratul Srinivasan
Ben Mildenhall
Sara Fridovich-Keil
Nithin Raghavan
Utkarsh Singhal
Ravi Ramamoorthi
Jonathan Barron

We show that passing input points through a simple Fourier feature mapping enables a multilayer perceptron (MLP) to learn high-frequency functions in low-dimensional problem domains. These results shed light on recent advances in computer vision and graphics that achieve state-of-the-art results by using MLPs to represent complex 3D objects and scenes. Using tools from the neural tangent kernel (NTK) literature, we show that a standard MLP has impractically slow convergence to high frequency signal components. To overcome this spectral bias, we use a Fourier feature mapping to transform the effective NTK into a stationary kernel with a tunable bandwidth. We suggest an approach for selecting problem-specific Fourier features that greatly improves the performance of MLPs for low-dimensional regression tasks relevant to the computer vision and graphics communities.

PDF Details