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20162023
most citedLearning to Infer Implicit Surfaces without 3D Supervision

88 citations · 222 across the 15 of their papers we have counts for

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23 papers · 1 filter

cs.CV2023★ 4 cited

Fast Image-based Neural Relighting with Translucency-Reflection Modeling

Shizhan Zhu, Shunsuke Saito, Aljaz Bozic +3

Image-based lighting (IBL) is a widely used technique that renders objects using a high dynamic range image or environment map. However, aggregating the irradiance at the object's…

cs.CV2023

NCHO: Unsupervised Learning for Neural 3D Composition of Humans and Objects

Taeksoo Kim, Shunsuke Saito, Hanbyul Joo

Deep generative models have been recently extended to synthesizing 3D digital humans. However, previous approaches treat clothed humans as a single chunk of geometry without consid…

cs.CV2023

MEGANE: Morphable Eyeglass and Avatar Network

Junxuan Li, Shunsuke Saito, Tomas Simon +3

Eyeglasses play an important role in the perception of identity. Authentic virtual representations of faces can benefit greatly from their inclusion. However, modeling the geometri…

cs.CV2023★ 1 cited

RelightableHands: Efficient Neural Relighting of Articulated Hand Models

Shun Iwase, Shunsuke Saito, Tomas Simon +7

We present the first neural relighting approach for rendering high-fidelity personalized hands that can be animated in real-time under novel illumination. Our approach adopts a tea…

cs.CV2022★ 2 cited

Neural Strands: Learning Hair Geometry and Appearance from Multi-View Images

Radu Alexandru Rosu, Shunsuke Saito, Ziyan Wang +3

We present Neural Strands, a novel learning framework for modeling accurate hair geometry and appearance from multi-view image inputs. The learned hair model can be rendered in rea…

cs.CV2022★ 1 cited

Drivable Volumetric Avatars using Texel-Aligned Features

Edoardo Remelli, Timur Bagautdinov, Shunsuke Saito +8

Photorealistic telepresence requires both high-fidelity body modeling and faithful driving to enable dynamically synthesized appearance that is indistinguishable from reality. In t…