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Stephan J. Garbin

10 accepted papers

2026

Relightable Holoported Characters: Capturing and Relighting Dynamic Human Performance from Sparse Views

Kunwar Maheep Singh, Jianchun Chen, Vladislav Golyanik, Stephan J. Garbin, Thabo Beeler, Rishabh Dabral +2
CVPR 2026

We present _Relightable Holoported Characters_ (RHC), a novel person-specific method for free-view rendering and relighting of full-body and highly dynamic humans solely observed from sparse-view RGB videos at inference. In contrast to classical one-light-at-a-time (OLAT)-based human relighting, our

Cited by 0Source ↗Scholar ↗
2025

ROGR: Relightable 3D Objects using Generative Relighting

Jiapeng Tang, Matthew Jacob Levine, Dor Verbin, Stephan J. Garbin, Matthias Nießner, Ricardo Martin Brualla +2
NeurIPS 2025spotlight

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 unde…

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2025

Synthetic Prior for Few-Shot Drivable Head Avatar Inversion

Wojciech Zielonka, Stephan J. Garbin, Alexandros Lattas, George Kopanas, Paulo Gotardo, Thabo Beeler +2
CVPR 2025poster

We present SynShot, a novel method for the few-shot inversion of a drivable head avatar based on a synthetic prior. We tackle three major challenges. First, training a controllable 3D generative network requires a large number of diverse sequences, for which pairs of images and high-quality tracked…

Cited by 1Source ↗PDF ↗Scholar ↗
2024

Nuvo: Neural UV Mapping for Unruly 3D Representations

Pratul Srinivasan*, Stephan J Garbin, Dor Verbin, Jonathan T Barron, Ben Mildenhall
ECCV 2024poster

"Existing UV mapping algorithms are designed to operate on well-behaved meshes, instead of the geometry representations produced by state-of-the-art 3D reconstruction and generation techniques. As such, applying these methods to the volume densities recovered by neural radiance fields and related te…

Cited by 15Source ↗PDF ↗Scholar ↗
2023

BlendFields: Few-Shot Example-Driven Facial Modeling

Kacper Kania, Stephan J. Garbin, Andrea Tagliasacchi, Virginia Estellers, Kwang Moo Yi, Julien Valentin +2
CVPR 2023poster

Generating faithful visualizations of human faces requires capturing both coarse and fine-level details of the face geometry and appearance. Existing methods are either data-driven, requiring an extensive corpus of data not publicly accessible to the research community, or fail to capture fine detai…

Cited by 8Source ↗PDF ↗Scholar ↗
2021

FastNeRF: High-Fidelity Neural Rendering at 200FPS

Stephan J. Garbin, Marek Kowalski, Matthew Johnson, Jamie Shotton, Julien Valentin
ICCV 2021poster

Recent work on Neural Radiance Fields (NeRF) showed how neural networks can be used to encode complex 3D environments that can be rendered photorealistically from novel viewpoints. Rendering these images is very computationally demanding and recent improvements are still a long way from enabling int…

Cited by 787PDF ↗Scholar ↗
2020

CONFIG: Controllable Neural Face Image Generation

Marek Kowalski, Stephan J. Garbin, Virginia Estellers, Tadas Baltrušaitis, Matthew Johnson, Jamie Shotton
ECCV 2020poster

Our ability to sample realistic natural images, particularly faces, has advanced by leaps and bounds in recent years, yet our ability to exert fine-tuned control over the generative process has lagged behind. If this new technology is to find practical uses, we need to achieve a level of control ove…

Cited by 114Source ↗PDF ↗code ↗Scholar ↗
2020

High Resolution Zero-Shot Domain Adaptation of Synthetically Rendered Face Images

Stephan J. Garbin, Marek Kowalski, Matthew Johnson, Jamie Shotton
ECCV 2020poster

Generating photorealistic images of human faces at scale remains a prohibitively difficult task using computer graphics approaches. This is because these require the simulation of light to be photorealistic, which in turn requires physically accurate modelling of geometry, materials, and light sourc…

Cited by 11Source ↗PDF ↗Scholar ↗
2017

Harmonic Networks: Deep Translation and Rotation Equivariance

Daniel E. Worrall, Stephan J. Garbin, Daniyar Turmukhambetov, Gabriel J. Brostow
CVPR 2017poster

Translating or rotating an input image should not affect the results of many computer vision tasks. Convolutional neural networks (CNNs) are already translation equivariant: input image translations produce proportionate feature map translations. This is not the case for rotations. Global rotation e…

Cited by 867PDF ↗Scholar ↗
2017

Interpretable Transformations With Encoder-Decoder Networks

Daniel E. Worrall, Stephan J. Garbin, Daniyar Turmukhambetov, Gabriel J. Brostow
ICCV 2017poster

Deep feature spaces have the capacity to encode complex transformations of their input data. However, understanding the relative feature-space relationship between two transformed encoded images is difficult. For instance, what is the relative feature space relationship between two rotated images? W…

Cited by 113PDF ↗Scholar ↗