Paris Mavroidis

dblp:66/7598 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1Graphics, computer vision, multimedia, augmented reality and games · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
3D vision · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › 3D vision › human mesh recovery
human body shape estimation
0.412019
FACSIMILE: Fast and Accurate Scans From an Image in Less Than a Second · ICCV 2019
Computer vision › 3D vision › 3d reconstruction
single-view 3d reconstruction
0.412019
FACSIMILE: Fast and Accurate Scans From an Image in Less Than a Second · ICCV 2019

Methods — techniques the papers use, named apart from their topics

surface normal loss · 0.4image translation network · 0.4
YearPublicationVenuePosition
2019 FACSIMILE: Fast and Accurate Scans From an Image in Less Than a Second
abstract
Current methods for body shape estimation either lack detail or require many images. They are usually architecturally complex and computationally expensive. We propose FACSIMILE (FAX), a method that estimates a detailed body from a single photo, lowering the bar for creating virtual representations of humans. Our approach is easy to implement and fast to execute, making it easily deployable. FAX uses an image-translation network which recovers geometry at the original resolution of the image. Counterintuitively, the main loss which drives FAX is on per-pixel surface normals instead of per-pixel depth, making it possible to estimate detailed body geometry without any depth supervision. We evaluate our approach both qualitatively and quantitatively, and compare with a state-of-the-art method.
Matthew Loper, Paris Mavroidis, Javier Romero 0002
ICCV4