VLDB 2026 Research / reviewers in the wild / expert
Paris Mavroidis
dblp:66/7598
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › 3D vision › human mesh recovery
human body shape estimation |
0.4 | 1 | 2019 | 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 |
0.4 | 1 | 2019 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | FACSIMILE: Fast and Accurate Scans From an Image in Less Than a SecondabstractCurrent 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 |
ICCV | 4 |