Ben Mildenhall

I work on problems in graphics and 3D computer vision at World Labs.

From 2021 to 2023, I was a research scientist at Google Research. I received my PhD from UC Berkeley in 2020, where I was advised by Ren Ng and supported by a Hertz fellowship.

In the summer of 2017, I was an intern in Marc Levoy's group in Google Research. In the summer of 2018, I worked with Rodrigo Ortiz-Cayon and Abhishek Kar at Fyusion. I did my undergrad at Stanford University and worked at Pixar Research in the summer of 2014.

Email  /  CV  /  Bio  /  Google Scholar  /  Twitter  /  GitHub

Highlights

Neural Radiance Fields
Many images to 3D

mip-NeRF 360 / Zip-NeRF
Bigger and better NeRF

DreamFusion
Text to 3D

ReconFusion
Few images to 3D

Research
NeRF-Casting: Improved View-Dependent Appearance with Consistent Reflections
Dor Verbin, Pratul Srinivasan, Peter Hedman, Benjamin Attal, Ben Mildenhall, Richard Szeliski, Jonathan T. Barron
SIGGRAPH Asia, 2024
project page / arXiv

Carefully casting reflection rays lets us synthesize photorealistic specularities in real-world scenes.

Flash Cache: Reducing Bias in Radiance Cache Based Inverse Rendering
Benjamin Attal, Dor Verbin, Ben Mildenhall, Peter Hedman, Jonathan T. Barron, Matthew O'Toole, Pratul P. Srinivasan
ECCV, 2024 (oral)
project page / arXiv

Radiance caching enables more physically accurate inverse rendering to recover geometry, materials, and lighting from RGB images of an object or scene.

Nuvo: Neural UV Mapping for Unruly 3D Representations
Pratul Srinivasan, Stephan J. Garbin, Dor Verbin, Jonathan T. Barron, Ben Mildenhall
ECCV, 2024
project page / video / arXiv

Use neural fields to recover editable UV mappings for challenging geometry (e.g. NeRFs, marching cubes meshes, DreamFusion).

Binary Opacity Grids: Capturing Fine Geometric Detail for Mesh-Based View Synthesis
Christian Reiser, Stephan J. Garbin, Pratul Srinivasan, Dor Verbin, Richard Szeliski,
Ben Mildenhall, Jonathan T. Barron, Peter Hedman*, Andreas Geiger*
SIGGRAPH, 2024
project page / video / arXiv

Apply anti-aliasing to a discrete opacity grid to enable highly-detailed mesh recovery.

ReconFusion: 3D Reconstruction with Diffusion Priors
Rundi Wu*, Ben Mildenhall*, Philipp Henzler, Keunhong Park, Ruiqi Gao, Daniel Watson, Pratul Srinivasan, Dor Verbin, Jonathan T. Barron, Ben Poole, Aleksander Holynski*
CVPR, 2024
project page / arXiv

Finetune an image diffusion model to accept multiview inputs, then use it to regularize radiance field reconstruction.

Eclipse: Disambiguating Illumination and Materials using Unintended Shadows
Dor Verbin, Ben Mildenhall, Peter Hedman, Jonathan T. Barron, Todd Zickler, Pratul Srinivasan
CVPR, 2024 (oral)
project page / video / arXiv

Shadows cast by unobserved occluders provide a high-frequency cue for recovering illumination and materials.

Generative Powers of Ten
Xiaojuan Wang, Janne Kontkanen, Brian Curless, Steve Seitz, Ira Kemelmacher,
Ben Mildenhall, Pratul Srinivasan, Dor Verbin, Aleksander Holynski
CVPR, 2024 (highlight)
project page / arXiv

Use a text-to-image model to generate consistent content across drastically varying scales.

CamP: Camera Preconditioning for Neural Radiance Fields
Keunhong Park, Philipp Henzler, Ben Mildenhall, Jonathan T. Barron, Ricardo Martin-Brualla
SIGGRAPH Asia, 2023
project page / arXiv

Preconditioning based on camera parameterization helps NeRF and camera extrinsics/intrinsics optimize better together.

Zip-NeRF: Anti-Aliased Grid-Based Neural Radiance Fields
Jonathan T. Barron, Ben Mildenhall, Dor Verbin, Pratul Srinivasan, Peter Hedman
ICCV, 2023 (Best Paper Finalist)
project page / video / arXiv

Combining mip-NeRF 360 and Instant NGP lets us reconstruct huge scenes.

DreamBooth3D: Subject-Driven Text-to-3D Generation
Amit Raj, Srinivas Kaza, Ben Poole, Michael Niemeyer, Nataniel Ruiz, Ben Mildenhall, Shiran Zada, Kfir Aberman, Michael Rubinstein, Jonathan T. Barron, Yuanzhen Li, Varun Jampani
ICCV, 2023
project page / arXiv

Combining DreamBooth (personalized text-to-image) and DreamFusion (text-to-3D) yields high-quality, subject-specific 3D assets with text-driven modifications

BakedSDF: Meshing Neural SDFs for Real-Time View Synthesis
Lior Yariv*, Peter Hedman*, Christian Reiser, Dor Verbin, Pratul Srinivasan, Richard Szeliski, Jonathan T. Barron, Ben Mildenhall
SIGGRAPH, 2023
project page / video / arXiv

We achieve real-time view synthesis by baking a high quality mesh and fine-tuning a lightweight appearance model on top.

MERF: Memory-Efficient Radiance Fields for Real-time View Synthesis in Unbounded Scenes
Christian Reiser, Richard Szeliski, Dor Verbin, Pratul Srinivasan, Ben Mildenhall, Andreas Geiger, Jonathan T. Barron, Peter Hedman
SIGGRAPH, 2023
project page / video / arXiv

We achieve real-time view synthesis using a volumetric rendering model with a compact representation combining a sparse 3D feature grid and 2D feature planes.

AligNeRF: High-Fidelity Neural Radiance Fields via Alignment-Aware Training
Yifan Jiang, Peter Hedman, Ben Mildenhall, Dejia Xu, Jonathan T. Barron, Zhangyang Wang, Tianfan Xue
CVPR, 2023
project page / arXiv

Accounting for misalignment due to scene motion or calibration errors improves NeRF reconstruction quality.

DreamFusion: Text-to-3D using 2D Diffusion
Ben Poole, Ajay Jain, Jonathan T. Barron, Ben Mildenhall
ICLR, 2023 (Outstanding Paper Award)
project page / arXiv / gallery

We optimize a NeRF from scratch using a pretrained text-to-image diffusion model to do text-to-3D generative modeling.

Fast and High-quality Image Denoising via Malleable Convolutions
Yifan Jiang, Bartlomiej Wronski, Ben Mildenhall, Jonathan T. Barron, Zhangyang Wang, Tianfan Xue
ECCV, 2022
project page / arXiv

We denoise images efficiently by predicting spatially-varying kernels at low resolution and using a fast fused op to jointly upsample and apply these kernels at full resolution.

NeRF in the Dark: High Dynamic Range View Synthesis from Noisy Raw Images
Ben Mildenhall, Peter Hedman, Ricardo Martin-Brualla, Pratul Srinivasan, Jonathan Barron
CVPR, 2022 (oral)
project page / arXiv / video / code

We train RawNeRF directly on linear raw camera images, enabling new HDR view synthesis applications and greatly increasing robustness to camera noise.

Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields
Jonathan T. Barron, Ben Mildenhall, Dor Verbin, Pratul Srinivasan, Peter Hedman
CVPR, 2022 (oral)
project page / arXiv / video / code

We extend mip-NeRF to produce photorealistic results on unbounded scenes.

Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance Fields
Dor Verbin, Peter Hedman, Ben Mildenhall, Todd Zickler, Jonathan T. Barron, Pratul Srinivasan
CVPR, 2022 (Best Student Paper Honorable Mention)
project page / arXiv / video / code

Explicitly modeling reflections in NeRF produces realistic shiny surfaces and accurate surface normals, and lets you edit materials.

Block-NeRF: Scalable Large Scene Neural View Synthesis
Matthew Tancik, Vincent Casser, Xinchen Yan, Sabeek Pradhan, Ben Mildenhall, Pratul Srinivasan, Jonathan T. Barron, Henrik Kretzschmar
CVPR, 2022 (oral)
project page / arXiv / video

We build city-scale scenes from many NeRFs, trained using millions of images.

RegNeRF: Regularizing Neural Radiance Fields for View Synthesis from Sparse Inputs
Michael Niemeyer, Jonathan T. Barron, Ben Mildenhall, Mehdi S. M. Sajjadi, Andreas Geiger, Noha Radwan
CVPR, 2022 (oral)
project page / arXiv / video

We regularize unseen views during optimization to enable view synthesis from as few as 3 input images.

Zero-Shot Text-Guided Object Generation with Dream Fields
Ajay Jain, Ben Mildenhall, Jonathan T. Barron, Pieter Abbeel, Ben Poole
CVPR, 2022
project page / arXiv / video / code

Supervising the CLIP embeddings of NeRF renderings allows us to generate 3D objects from text prompts alone.

Dense Depth Priors for Neural Radiance Fields from Sparse Input Views
Barbara Roessle, Jonathan T. Barron, Ben Mildenhall, Pratul Srinivasan, Matthias Nießner
CVPR, 2022
arXiv / video

We apply dense depth completion techniques to freely-available sparse stereo data to guide NeRF reconstructions from few input images.

Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields
Jonathan T. Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin-Brualla, Pratul Srinivasan
ICCV, 2021 (Best Paper Honorable Mention)
project page / arXiv / video / code

We prefilter the positional encoding function and train NeRF to generate anti-aliased renderings.

Baking Neural Radiance Fields for Real-Time View Synthesis
Peter Hedman, Pratul Srinivasan, Ben Mildenhall, Jonathan T. Barron, Paul Debevec
ICCV, 2021 (oral)
project page / arXiv / video / demo

We bake a trained NeRF into a sparse voxel grid of colors and features in order to render it in real-time.

Learned Initializations for Optimizing Coordinate-Based Neural Representations
Matthew Tancik*, Ben Mildenhall*, Terrance Wang, Divi Schmidt, Pratul Srinivasan, Jonathan T. Barron, Ren Ng
CVPR, 2021 (oral)
project page / arXiv / video / code

We use meta-learning to find weight initializations for coordinate-based MLPs that allow them to converge faster and generalize better.

NeRV: Neural Reflectance and Visibility Fields for Relighting and View Synthesis
Pratul Srinivasan, Boyang Deng, Xiuming Zhang, Matthew Tancik, Ben Mildenhall, Jonathan T. Barron
CVPR, 2021
project page / arXiv / video

We recover relightable NeRF-like models using neural approximations of expensive visibility integrals, so we can simulate complex volumetric light transport during training.

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 T. Barron, Ren Ng
NeurIPS, 2020 (spotlight)
project page / arXiv / code

We demonstrate that composing fully-connected networks with a simple Fourier feature mapping allows them to learn much high frequency functions.

Neural Reflectance Fields for Appearance Acquisition
Sai Bi*, Zexiang Xu*, Pratul Srinivasan, Ben Mildenhall, Kalyan Sunkavalli, Milos Hasan, Yannick Hold-Geoffroy, David Kriegman, Ravi Ramamoorthi
arXiv, 2020
arXiv

We recover relightable NeRF-like models by predicting per-location BRDFs and surface normals, and marching light rays through the NeRV volume to compute visibility.

NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
Ben Mildenhall*, Pratul Srinivasan*, Matthew Tancik*, Jonathan T. Barron, Ravi Ramamoorthi, Ren Ng
ECCV, 2020 (Best Paper Honorable Mention)
project page / arXiv / video / talk / code / two minute papers / papers with code / wired

We optimize a simple fully-connected network to represent a single scene as a volume, then use volume rendering to do view synthesis.

Deep Multi Depth Panoramas for View Synthesis
Kai-En Lin, Zexiang Xu, Ben Mildenhall, Pratul Srinivasan, Yannick Hold-Geoffroy, Stephen DiVerdi, Qi Sun, Kalyan Sunkavalli, Ravi Ramamoorthi
ECCV, 2020
arXiv / video

We represent scenes as multi-layer panoramas with depth for VR view synthesis.

Lighthouse: Predicting Lighting Volumes for Spatially-Coherent Illumination
Pratul Srinivasan*, Ben Mildenhall*, Matthew Tancik, Jonathan T. Barron, Richard Tucker, Noah Snavely
CVPR, 2020
project page / arXiv / video / code

We predict a volume from an input stereo pair that can be used to calculate incident lighting at any 3D point within a scene.

StegaStamp: Invisible Hyperlinks in Physical Photographs
Matthew Tancik*, Ben Mildenhall*, Ren Ng
CVPR, 2020
project page / arXiv / video / code

We can hide hyperlinks in natural images to create aesthetically pleasing barcodes.

Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelines
Ben Mildenhall*, Pratul Srinivasan*, Rodrigo Ortiz-Cayon, Nima Khademi Kalantari, Ravi Ramamoorthi, Ren Ng, Abhishek Kar
SIGGRAPH, 2019
project page / arXiv / video / code

We develop and analyze a deep learning method for rendering novel views of complex real world scenes.

Unprocessing Images for Learned Raw Denoising
Tim Brooks, Ben Mildenhall, Tianfan Xue, Jiawen Chen, Dillon Sharlet, Jonathan T. Barron
CVPR, 2019 (oral)
project page / arXiv

We can learn a better denoising model by processing and unprocessing images the same way a camera does.

Burst Denoising with Kernel Prediction Networks
Ben Mildenhall, Jonathan T. Barron, Jiawen Chen, Dillon Sharlet, Ren Ng, Robert Carroll
CVPR, 2018 (spotlight)
project page / arXiv / code

We train a network to predict linear kernels that denoise bursts of raw linear images.

DiffuserCam: Lensless Single-exposure 3D Imaging
Nick Antipa, Grace Kuo, Reinhard Heckel, Ben Mildenhall, Emrah Bostan, Ren Ng, Laura Waller
Optica, 2018
project page / arXiv

Using a diffuser instead of a lens lets you recover 3D in a single exposure.

microflakes

Approximations for the distribution of microflake normals
Nelson Max, Tom Duff, Ben Mildenhall, Yajie Yan
The Visual Computer, 2017

We precompute microflake approximations to make rendering large meshes at a distance more efficient.

sosmc

Controlling Procedural Modeling Programs with Stochastically-Ordered Sequential Monte Carlo
Daniel Ritchie, Ben Mildenhall, Noah D. Goodman, Pat Hanrahan
SIGGRAPH, 2015

We improve control over the output of highly-variable procedural modeling programs by using SOSMC to provide incremental feedback on partially-generated models.

Course Projects
prl

Extending the PBRT Renderer to Support Volumetric Light Sources
Grand prize in CS384B rendering competition, Spring 2013

Adding support for multicolored, nonhomogeneous, emissive volumes in PBRT 2's path tracing integrator.

prl

Ray Tracer
CS148 assignment, Fall 2012

Implemented a ray tracer with reflections, refractions, soft shadows, and depth of field.

Teaching
dragon

CS184 - Summer 2020 (Co-instructor)

CS184 - Spring 2017 (GSI)

CS184 - Spring 2016 (GSI)

Music
piano

Rachmaninoff - Etude-Tableau Op. 39 No. 5
Encore after a salon recital, Spring 2019

Rachmaninoff - Rhapsody on a Theme of Paganini
With Stanford Symphony Orchestra, Spring 2015

Brahms - Piano Quintet
Recital at Stanford, Winter 2015


Yep it's another Jon Barron website.
Last updated August 2024.