hello nagadomi, one of pioneer of related research
and researchers that studies on this field
I have some thinking about this detection task
Detection
in reality research on detection has been extended to, target detection, face recognition, OCR and other detection tasks, here I'm not going to expand detail or reference some example(maybe later will add)
There are also many related animation tasks that make use of this tool
For example, character recognition systems, as well as generation tasks, character design tools, character animation, etc.
from 2016 to 2021
But they result not so good , especially the generation based
the research is almost stagnant, and there is not much research on it, these result can't be commercial
that is because definition of detection (anime face) is no longer suitable for these tasks
in reality research tasks also have no counterpart in anime
and it is not possible to clean, split and extract useful data to make a dataset with detection and classification tools
Downstream tasks
I believe that there is now a need to redefine and design different datasets for the following tasks
Tool cleaning to facilitate the creation of semantic, annotated datasets
(face, full body) character design (diversity [data], quality, creative [decoupling])
Anime character recognition systems(include famous, not very popular, unpopular and manga, original)
Automatic animation, AI warp, motion -> talkinghead
anime stylization on real faces
(this*doesnotexist, large-scale generative pre-train) anime on generative model
As far as I know, every detector is now working for one purpose only, to detect anime faces
The definition is very vague, and the
there are various types sources (animation/manga/game/draw)
anime is mainly express on characters, those media type also has varies Form of expression
As a data processing tool
The value of the detector in the anime deeplearning very high, not just for detect faces, but as a data processing tool, and I think the detector needs multiple requirements
wild hard collect (in-game CG and more)
positive and negative samples to provide
angle of view, deformation, degree of perspective
what components and what proportion of them and how it composition
the degree of degradation of the image (colour, shape, light and shadow)
provide some attributes for downstream (for the generation task or recognition system etc.)
appendix - What is Anime
I studied anime data for a long time (Data mining) but now I found it was easy to define
anime-look(based creation ecology) character with multi-media (animaiton, manga, game, draw, novel)
so .. many vtubers also are anime
character works , derivative work , fanmade/fanart(mostly is draw ,meme etc.) , these are peripheral anime ecology
and can expression on real world,eg : cosplay , doujinshi circle , sale goods , event etc..
and those type of character with multi-media define can generalized to Fictional characters based entertainment
hello nagadomi, one of pioneer of related research and researchers that studies on this field
I have some thinking about this detection task
Detection
in reality research on detection has been extended to, target detection, face recognition, OCR and other detection tasks, here I'm not going to expand detail or reference some example(maybe later will add)
There are also many related animation tasks that make use of this tool For example, character recognition systems, as well as generation tasks, character design tools, character animation, etc.
from 2016 to 2021
But they result not so good , especially the generation based the research is almost stagnant, and there is not much research on it, these result can't be commercial
that is because definition of detection (anime face) is no longer suitable for these tasks in reality research tasks also have no counterpart in anime and it is not possible to clean, split and extract useful data to make a dataset with detection and classification tools
Downstream tasks
I believe that there is now a need to redefine and design different datasets for the following tasks
As far as I know, every detector is now working for one purpose only, to detect anime faces The definition is very vague, and the
As a data processing tool
The value of the detector in the anime deeplearning very high, not just for detect faces, but as a data processing tool, and I think the detector needs multiple requirements
appendix - What is Anime
I studied anime data for a long time (Data mining) but now I found it was easy to define
anime-look(based creation ecology) character with multi-media (animaiton, manga, game, draw, novel)
character works , derivative work , fanmade/fanart(mostly is draw ,meme etc.) , these are peripheral anime ecology and can expression on real world,eg : cosplay , doujinshi circle , sale goods , event etc..
and those type of character with multi-media define can generalized to Fictional characters based entertainment
Thank for you reading