Main

processing priority

3

site type

5 (wiki-type site, growing by topic rather than chronologically)

review version

11

html import

20 (imported)

Events

first seen date

2024-08-27 04:23:52

expired found date

-

created at

2024-08-27 04:23:52

updated at

2024-09-17 12:24:33

Domain name statistics

length

30

crc

38395

tld

86

nm parts

0

nm random digits

0

nm rare letters

0

Connections

is subdomain of id

87719371 (github.io)

previous id

0

replaced with id

0

related id

-

dns primary id

0

dns alternative id

0

lifecycle status

0 (unclassified, or currently active)

Subdomains and pages

deleted subdomains

0

page imported products

0

page imported random

0

page imported parking

0

Error counters

count skipped due to recent timeouts on the same server IP

0

count content received but rejected due to 11-799

0

count dns errors

0

count cert errors

0

count timeouts

0

count http 429

0

count http 404

0

count http 403

0

count http 5xx

0

next operation date

-

Server

server bits

server ip

-

Mainpage statistics

mp import status

20

mp rejected date

-

mp saved date

-

mp size orig

8594

mp size raw text

2433

mp inner links count

1

mp inner links status

10 (links queued, awaiting import)

Open Graph

title

description

image

site name

author

updated

2026-02-25 18:02:20

raw text

Projectpage of Animatable Gaussians Animatable Gaussians: Learning Pose-dependent Gaussian Maps for High-fidelity Human Avatar Modeling CVPR 2024 Zhe Li 1 , Zerong Zheng 2 , Lizhen Wang 1 , Yebin Liu 1 1 Tsinghua University     2 NNKosmos Technology Paper Video Code Extension for Relighting Abstract Modeling animatable human avatars from RGB videos is a long-standing and challenging problem. Recent works usually adopt MLP-based neural radiance fields (NeRF) to represent 3D humans, but it remains difficult for pure MLPs to regress pose-dependent garment details. To this end, we introduce Animatable Gaussians, a new avatar representation that leverages powerful 2D CNNs and 3D Gaussian splatting to create high-fidelity avatars. To associate 3D Gaussians with the animatable avatar, we learn a parametric template from the input videos, and then parameterize the template on two front & back canonical Gaussian maps where each pixel represents a 3D Gaussian. ...

Text analysis

redirect type

0 (-)

block type

0 (no issues)

detected language

1 (English)

category id

Pozostałe (16)

index version

1

spam phrases

0

Text statistics

text nonlatin

0

text cyrillic

0

text characters

1929

text words

326

text unique words

189

text lines

42

text sentences

14

text paragraphs

3

text words per sentence

23

text matched phrases

0

text matched dictionaries

0

RSS

rss path

rss status

1 (priority 1 already searched, no matches found)

rss found date

-

rss size orig

0

rss items

0

rss spam phrases

0

rss detected language

0 (awaiting analysis)

inbefore feed id

-

inbefore status

0 (new)

Sitemap

sitemap path

sitemap status

1 (priority 1 already searched, no matches found)

sitemap review version

2

sitemap urls count

0

sitemap urls adult

0

sitemap filtered products

0

sitemap filtered videos

0

sitemap found date

-

sitemap process date

-

sitemap first import date

-

sitemap last import date

-