#zhang chuchu

[silylilyanimove]

[justanie]

I've been so involved in going back to school that I can barely focus on my drawing planned for these dates...in the meantime I'll leave this old drawing from 2021!!

[waffulaa]

VOCALOID4 Zhang Chuchu reference sheet.

[vocaloidfactoftheday]

Zhang Chuchu's voicebank has been preserved and uploaded on the Internet Archive (though due to the discontinuation of her serial codes, she can only be used as a trial).

Her voicebank is installed under the name "Carenna Crystal." This name is used for an apparently identical sounding Chinese VOCALOID5 voicebank that remains unreleased, and never officially announced.

(source: vocarchive on archive.org)

[vocalsynthbdays]

happy birthday zhang chuchu(vocaloid 4) !!!!!!! [aug 9]

Zhang Chuchu is a chinese synth developed by Shanghai Wangcheng in collaboration with Migu Comics, and released on 30 apr 2018, though her character birthday is 9 aug. shes voiced by Wan Su, who was chosen through an audition, and her illustrator has not been revealed. chuchu is originally from a manhua called Chǔchǔ Dòng Rén, published by Migu Comics, in which she looked very different. shes a private vb so only select people can use her. she is 21yo and 168cm tall.

chuchu in Chǔchǔ Dòng Rén

logo

ref

[mvvocaloidgifs]

【苍穹·墨清弦·章楚楚原创曲】Hysteria【SinghP】

[welcometothejianghu]

A little Legend of Fei In Real Life tonight, because I feel like looking at pictures of attractive people. (Skipping the two mains, because it's well-documented what they look like out of costume.)

Zhang Hui Wen / Wu Chuchu

Chen Ruo Xuan / Li Sheng

Zhang Xin Yu / Yang Jin

Leng Ji Yuan / Ying Hecong

Sun Jian / Yin Pei

Zhou Jie Qiong / Li Yan

Geng Le / Shen Tianshu

Guo Xin / Mu Xiaoqiao

Dong Xuan / Duan Jiuniang

[missfangirll]

☆°･:*:･｡★ Videos ★ ｡･:*:･°☆

★ 陈情令 | The Untamed ★

☆ Nothing More (Xiao Xingchen) ☆ Memories (Xiao Xingchen/Song Lan) ☆ Der Weg (Xiao Xingchen/Song Lan)   

★ 莲花楼 | Mysterious Lotus Casebook ★

☆ Never your friend (Fang Duobing/Li Lianhua/Di Feisheng) ☆ Coming Home (Fang Duobing/Li Lianhua/Di Feisheng) ☆ Nothing’s Like Home (Fang Duobing/Li Lianhua/Di Feisheng) ☆ Homesick  (Fang Duobing/Li Lianhua/Di Feisheng)

    ★ 盗墓笔记 | The Lost Tomb ★

☆ Read my Lips (The Lost Tomb Restart: The Grave in the Abyss, Hei Xiazi/Xiao Hua) ☆ Saving Grace (The Lost Tomb Restart: The Grave in the Abyss, Hei Xiazi/Xiao Hua) ☆ Never Again (The Lost Tomb Restart: The Grave in the Abyss, Hei Xiazi/Xiao Hua) ☆ Like Real People Do (The Lost Tomb: Ultimate Note, Hei Xiazi/Xiao Hua) ☆ Ugly Heart (The Mystic Nine: Qing Shan Hai Tang, Yan Sanxing) ☆ The Storm (The Lost Tomb Restart: The Grave in the Abyss, Hei Xiazi/Xiao Hua) ☆ Unstoppable (The Lost Tomb Restart: The Grave in the Abyss, Hei Xiazi/Xiao Hua) ☆ Someone you loved (The Lost Tomb: Sand Sea, Wu Xie/Zhang Qiling) ☆ Someone you loved (ver.2) (The Lost Tomb: Sand Sea, Wu Xie/Zhang Qiling) ☆ My Clan (The Lost Tomb & The Mystic Nine: Zhang Family tribute) ☆ The Anthem (The Lost Tomb: Sand Sea, Su Nan) ☆ The Kill (The Mystic Nine: Qing Shan Hai Tang, Yan Sanxing) ☆ Poker Face (The Lost Tomb, Zhang Qiling) ☆ Material Girl (The Lost Tomb: Ultimate Note, Hei Xiazi) ☆ Always been you (The Lost Tomb: Reunion, Kan Jian/Liu Sang) ☆ With Iris (The Lost Tomb: Reunion, Iron Triangle) ☆ A Thousand Years (The Lost Tomb: Reunion, Kan Jian/Liu Sang, Reincarnation AU) ☆ Say Goodbye (The Lost Tomb: Reunion, Wu Xie & Wu Sanxing) ☆ Two (The Lost Tomb: Reunion, Pangzi) ☆ Runaway (The Lost Tomb, Wu Xie/Zhang Qiling) ☆ Stay Alive (The Lost Tomb: Reunion, Wu Xie/Zhang Qiling) ☆ I will not bow (The Lost Tomb: Reunion, Liu Sang) ☆ Counting stars (The Lost Tomb: Sand Sea, Liang Wan/Zhang Rishan) ☆ Seven Wonders (The Lost Tomb: Ultimate Note, Hei Xiazi/Xiao Hua) ☆ Flatline (The Lost Tomb: Ultimate Note, Hei Xiazi/Xiao Hua) ☆ I’ve been blind (The Lost Tomb: Ultimate Note, Hei Xiazi/Xiao Hua) ☆ Rock Bottom (The Lost Tomb: Ultimate Note, Hei Xiazi/Xiao Hua) ☆ Where do lovers go? (The Lost Tomb: Reunion, Wu Xie/Zhang Qiling) ☆ King and Lionheart (The Mystic Nine, Fo Ye & Zhang Rishan) ☆ Time well wasted (The Lost Tomb 2, Wu Xie/Xiao Hua) ☆ The Last Snowfall (Tibetan Sea Flower, Zhang Qiling) ☆ Behind the mask (Tibetan Sea Flower, Zhang Haike & Zhang Haixing) ☆ Warriors (Tibetan Sea Flower, Scorpion Team) ☆ You look good in white (The Lost Tomb Restart: The Grave in the Abyss, Hei Xiazi/Xiao Hua) ☆ All about that bass (DMBJ tiddie tattoo appreciation) ☆ Nice to meet you (The Lost Tomb: Reunion, Wang Pangzi/Ye Piaopiao) ☆ Foolish One (The Lost Tomb: Reunion, Hei Xiazi&/Chuchu) ☆ Accidentally in love (The Lost Tomb: Ultimate Note, Hei Xiazi/Xiao Hua) ☆ Pieces (Tibetan Sea Flower, Zhang Qiling/Wu Xie) ☆ 99 Problems (The Lost Tomb: Ultimate Note, Wu Xie) ☆ Glitter and Gold (The Lost Tomb, Zhang Qiling) ☆ Dear Future Husband (The Lost Tomb: Ultimate Note, Hei Xiazi/Xiao Hua)

     ★ 镇魂 | Guardian ★

☆ Favourite Song (Shen Wei/Zhao Yunlan) ☆ Eternity (Shen Wei/Zhao Yunlan) ☆ Shallow (Shen Wei/Zhao Yunlan) ☆ Come to me (Shen Wei/Zhao Yunlan) ☆ I’ll wait (Chu Shuzhi/Guo Changcheng) ☆ Alone (Ye Zun) ☆ Die for you (Shen Wei/Zhao Yunlan) ☆ We won't be falling (Shen Wei/Zhao Yunlan) ☆ Fear of the Water (Shen Wei/Zhao Yunlan) ☆ Silence (Shen Wei/Zhao Yunlan) ☆ We found love (Shen Wei/Zhao Yunlan) ☆ Enough to go by (Shen Wei/Zhao Yunlan) ☆ All the love that I ever needed (Shen Wei/Zhao Yunlan) ☆ These old wheels (Shen Wei/Zhao Yunlan) ☆ Someone to stay (Shen Wei/Zhao Yunlan)

     ★ S.C.I.谜案集 | S.C.I. Mystery ★

☆ True Love (Bai Yutong/Zhan Yao)

               ★ 天官赐福 | Heaven Officials’s Blessing ★

☆ Coming Home (Xie Lian/Hua Cheng)

     ★ 苍兰诀  | Love between Fairy and Devil ★

☆ I used to rule the world (Dongfang Qingcang & Xunfeng)

    ★ KinnPorsche ★

☆ Half a Man (Vegas/Pete) ☆ Poison (Vegas/Pete) ☆ Would you come home (Vegas/Pete)

     ☆ﾟ･:*:･｡★ﾟ You can always send an ask! ★ ｡･:*:･ﾟ☆ ☆

[staincastle]

faz icon da zhang jingyi, por favor!! e queria dizer aqui que sua galeria é a minha fav de todo o tumblr, seus icons sempre são tão maravilhosos que fico até triste de não poder usar todos de uma vez só 😞

olá chuchu!!!! [pani no sistema alguém me desconfigurou] tô aosbdkdn me deixou sem reação de verdade, fiquei >imensamente< feliz por receber essa ask vius, não tenho nem palavras direito pra descrever oq estou sentindo 🫂💗 qjshkd e aqui estão seu icons 📦, muito obrigado por pedir e dar tanto carinho pra galeria, você é incrível 🩷🤲🏻

[confluencextras]

[Image Description: Nine square transparents of different Vocaloids. They are Aoki Lapis, Tone Rion, Anri Rune, Zhang Chuchu, Yuecheng, and Yao Luniang. /end ID]

[sunaleisocial]

MIT engineers help multirobot systems stay in the safety zone

New Post has been published on https://sunalei.org/news/mit-engineers-help-multirobot-systems-stay-in-the-safety-zone/

MIT engineers help multirobot systems stay in the safety zone

Drone shows are an increasingly popular form of large-scale light display. These shows incorporate hundreds to thousands of airborne bots, each programmed to fly in paths that together form intricate shapes and patterns across the sky. When they go as planned, drone shows can be spectacular. But when one or more drones malfunction, as has happened recently in Florida, New York, and elsewhere, they can be a serious hazard to spectators on the ground.

Drone show accidents highlight the challenges of maintaining safety in what engineers call “multiagent systems” — systems of multiple coordinated, collaborative, and computer-programmed agents, such as robots, drones, and self-driving cars.

Now, a team of MIT engineers has developed a training method for multiagent systems that can guarantee their safe operation in crowded environments. The researchers found that once the method is used to train a small number of agents, the safety margins and controls learned by those agents can automatically scale to any larger number of agents, in a way that ensures the safety of the system as a whole.

In real-world demonstrations, the team trained a small number of palm-sized drones to safely carry out different objectives, from simultaneously switching positions midflight to landing on designated moving vehicles on the ground. In simulations, the researchers showed that the same programs, trained on a few drones, could be copied and scaled up to thousands of drones, enabling a large system of agents to safely accomplish the same tasks.

A team of Crazyflie drones use MIT algorithm to safely switch positions while avoiding obstacles

Image: Courtesy of the researchers

Previous item Next item

“This could be a standard for any application that requires a team of agents, such as warehouse robots, search-and-rescue drones, and self-driving cars,” says Chuchu Fan, associate professor of aeronautics and astronautics at MIT. “This provides a shield, or safety filter, saying each agent can continue with their mission, and we’ll tell you how to be safe.”

Fan and her colleagues report on their new method in a study appearing this month in the journal IEEE Transactions on Robotics. The study’s co-authors are MIT graduate students Songyuan Zhang and Oswin So as well as former MIT postdoc Kunal Garg, who is now an assistant professor at Arizona State University.

Mall margins

When engineers design for safety in any multiagent system, they typically have to consider the potential paths of every single agent with respect to every other agent in the system. This pair-wise path-planning is a time-consuming and computationally expensive process. And even then, safety is not guaranteed.

“In a drone show, each drone is given a specific trajectory — a set of waypoints and a set of times — and then they essentially close their eyes and follow the plan,” says Zhang, the study’s lead author. “Since they only know where they have to be and at what time, if there are unexpected things that happen, they don’t know how to adapt.”

The MIT team looked instead to develop a method to train a small number of agents to maneuver safely, in a way that could efficiently scale to any number of agents in the system. And, rather than plan specific paths for individual agents, the method would enable agents to continually map their safety margins, or boundaries beyond which they might be unsafe. An agent could then take any number of paths to accomplish its task, as long as it stays within its safety margins.

In some sense, the team says the method is similar to how humans intuitively navigate their surroundings.

“Say you’re in a really crowded shopping mall,” So explains. “You don’t care about anyone beyond the people who are in your immediate neighborhood, like the 5 meters surrounding you, in terms of getting around safely and not bumping into anyone. Our work takes a similar local approach.”

Safety barrier

In their new study, the team presents their method, GCBF+, which stands for “Graph Control Barrier Function.” A barrier function is a mathematical term used in robotics that calculates a sort of safety barrier, or a boundary beyond which an agent has a high probability of being unsafe. For any given agent, this safety zone can change moment to moment, as the agent moves among other agents that are themselves moving within the system.

When designers calculate barrier functions for any one agent in a multiagent system, they typically have to take into account the potential paths and interactions with every other agent in the system. Instead, the MIT team’s method calculates the safety zones of just a handful of agents, in a way that is accurate enough to represent the dynamics of many more agents in the system.

“Then we can sort of copy-paste this barrier function for every single agent, and then suddenly we have a graph of safety zones that works for any number of agents in the system,” So says.

To calculate an agent’s barrier function, the team’s method first takes into account an agent’s “sensing radius,” or how much of the surroundings an agent can observe, depending on its sensor capabilities. Just as in the shopping mall analogy, the researchers assume that the agent only cares about the agents that are within its sensing radius, in terms of keeping safe and avoiding collisions with those agents.

Then, using computer models that capture an agent’s particular mechanical capabilities and limits, the team simulates a “controller,” or a set of instructions for how the agent and a handful of similar agents should move around. They then run simulations of multiple agents moving along certain trajectories, and record whether and how they collide or otherwise interact.

“Once we have these trajectories, we can compute some laws that we want to minimize, like say, how many safety violations we have in the current controller,” Zhang says. “Then we update the controller to be safer.”

In this way, a controller can be programmed into actual agents, which would enable them to continually map their safety zone based on any other agents they can sense in their immediate surroundings, and then move within that safety zone to accomplish their task.

“Our controller is reactive,” Fan says. “We don’t preplan a path beforehand. Our controller is constantly taking in information about where an agent is going, what is its velocity, how fast other drones are going. It’s using all this information to come up with a plan on the fly and it’s replanning every time. So, if the situation changes, it’s always able to adapt to stay safe.”

The team demonstrated GCBF+ on a system of eight Crazyflies — lightweight, palm-sized quadrotor drones that they tasked with flying and switching positions in midair. If the drones were to do so by taking the straightest path, they would surely collide. But after training with the team’s method, the drones were able to make real-time adjustments to maneuver around each other, keeping within their respective safety zones, to successfully switch positions on the fly.

In similar fashion, the team tasked the drones with flying around, then landing on specific Turtlebots — wheeled robots with shell-like tops. The Turtlebots drove continuously around in a large circle, and the Crazyflies were able to avoid colliding with each other as they made their landings.

“Using our framework, we only need to give the drones their destinations instead of the whole collision-free trajectory, and the drones can figure out how to arrive at their destinations without collision themselves,” says Fan, who envisions the method could be applied to any multiagent system to guarantee its safety, including collision avoidance systems in drone shows, warehouse robots, autonomous driving vehicles, and drone delivery systems.

This work was partly supported by the U.S. National Science Foundation, MIT Lincoln Laboratory under the Safety in Aerobatic Flight Regimes (SAFR) program, and the Defence Science and Technology Agency of Singapore.

[jcmarchi]

MIT engineers help multirobot systems stay in the safety zone

New Post has been published on https://thedigitalinsider.com/mit-engineers-help-multirobot-systems-stay-in-the-safety-zone/

MIT engineers help multirobot systems stay in the safety zone

Drone shows are an increasingly popular form of large-scale light display. These shows incorporate hundreds to thousands of airborne bots, each programmed to fly in paths that together form intricate shapes and patterns across the sky. When they go as planned, drone shows can be spectacular. But when one or more drones malfunction, as has happened recently in Florida, New York, and elsewhere, they can be a serious hazard to spectators on the ground.

Drone show accidents highlight the challenges of maintaining safety in what engineers call “multiagent systems” — systems of multiple coordinated, collaborative, and computer-programmed agents, such as robots, drones, and self-driving cars.

Now, a team of MIT engineers has developed a training method for multiagent systems that can guarantee their safe operation in crowded environments. The researchers found that once the method is used to train a small number of agents, the safety margins and controls learned by those agents can automatically scale to any larger number of agents, in a way that ensures the safety of the system as a whole.

In real-world demonstrations, the team trained a small number of palm-sized drones to safely carry out different objectives, from simultaneously switching positions midflight to landing on designated moving vehicles on the ground. In simulations, the researchers showed that the same programs, trained on a few drones, could be copied and scaled up to thousands of drones, enabling a large system of agents to safely accomplish the same tasks.

A team of Crazyflie drones use MIT algorithm to safely switch positions while avoiding obstacles

Image: Courtesy of the researchers

Previous item Next item

“This could be a standard for any application that requires a team of agents, such as warehouse robots, search-and-rescue drones, and self-driving cars,” says Chuchu Fan, associate professor of aeronautics and astronautics at MIT. “This provides a shield, or safety filter, saying each agent can continue with their mission, and we’ll tell you how to be safe.”

Fan and her colleagues report on their new method in a study appearing this month in the journal IEEE Transactions on Robotics. The study’s co-authors are MIT graduate students Songyuan Zhang and Oswin So as well as former MIT postdoc Kunal Garg, who is now an assistant professor at Arizona State University.

Mall margins

When engineers design for safety in any multiagent system, they typically have to consider the potential paths of every single agent with respect to every other agent in the system. This pair-wise path-planning is a time-consuming and computationally expensive process. And even then, safety is not guaranteed.

“In a drone show, each drone is given a specific trajectory — a set of waypoints and a set of times — and then they essentially close their eyes and follow the plan,” says Zhang, the study’s lead author. “Since they only know where they have to be and at what time, if there are unexpected things that happen, they don’t know how to adapt.”

The MIT team looked instead to develop a method to train a small number of agents to maneuver safely, in a way that could efficiently scale to any number of agents in the system. And, rather than plan specific paths for individual agents, the method would enable agents to continually map their safety margins, or boundaries beyond which they might be unsafe. An agent could then take any number of paths to accomplish its task, as long as it stays within its safety margins.

In some sense, the team says the method is similar to how humans intuitively navigate their surroundings.

“Say you’re in a really crowded shopping mall,” So explains. “You don’t care about anyone beyond the people who are in your immediate neighborhood, like the 5 meters surrounding you, in terms of getting around safely and not bumping into anyone. Our work takes a similar local approach.”

Safety barrier

In their new study, the team presents their method, GCBF+, which stands for “Graph Control Barrier Function.” A barrier function is a mathematical term used in robotics that calculates a sort of safety barrier, or a boundary beyond which an agent has a high probability of being unsafe. For any given agent, this safety zone can change moment to moment, as the agent moves among other agents that are themselves moving within the system.

When designers calculate barrier functions for any one agent in a multiagent system, they typically have to take into account the potential paths and interactions with every other agent in the system. Instead, the MIT team’s method calculates the safety zones of just a handful of agents, in a way that is accurate enough to represent the dynamics of many more agents in the system.

“Then we can sort of copy-paste this barrier function for every single agent, and then suddenly we have a graph of safety zones that works for any number of agents in the system,” So says.

To calculate an agent’s barrier function, the team’s method first takes into account an agent’s “sensing radius,” or how much of the surroundings an agent can observe, depending on its sensor capabilities. Just as in the shopping mall analogy, the researchers assume that the agent only cares about the agents that are within its sensing radius, in terms of keeping safe and avoiding collisions with those agents.

Then, using computer models that capture an agent’s particular mechanical capabilities and limits, the team simulates a “controller,” or a set of instructions for how the agent and a handful of similar agents should move around. They then run simulations of multiple agents moving along certain trajectories, and record whether and how they collide or otherwise interact.

“Once we have these trajectories, we can compute some laws that we want to minimize, like say, how many safety violations we have in the current controller,” Zhang says. “Then we update the controller to be safer.”

In this way, a controller can be programmed into actual agents, which would enable them to continually map their safety zone based on any other agents they can sense in their immediate surroundings, and then move within that safety zone to accomplish their task.

“Our controller is reactive,” Fan says. “We don’t preplan a path beforehand. Our controller is constantly taking in information about where an agent is going, what is its velocity, how fast other drones are going. It’s using all this information to come up with a plan on the fly and it’s replanning every time. So, if the situation changes, it’s always able to adapt to stay safe.”

The team demonstrated GCBF+ on a system of eight Crazyflies — lightweight, palm-sized quadrotor drones that they tasked with flying and switching positions in midair. If the drones were to do so by taking the straightest path, they would surely collide. But after training with the team’s method, the drones were able to make real-time adjustments to maneuver around each other, keeping within their respective safety zones, to successfully switch positions on the fly.

In similar fashion, the team tasked the drones with flying around, then landing on specific Turtlebots — wheeled robots with shell-like tops. The Turtlebots drove continuously around in a large circle, and the Crazyflies were able to avoid colliding with each other as they made their landings.

“Using our framework, we only need to give the drones their destinations instead of the whole collision-free trajectory, and the drones can figure out how to arrive at their destinations without collision themselves,” says Fan, who envisions the method could be applied to any multiagent system to guarantee its safety, including collision avoidance systems in drone shows, warehouse robots, autonomous driving vehicles, and drone delivery systems.

This work was partly supported by the U.S. National Science Foundation, MIT Lincoln Laboratory under the Safety in Aerobatic Flight Regimes (SAFR) program, and the Defence Science and Technology Agency of Singapore.

[celebratedaily]

August 9th - Fictional Birthdays

Yellow Ranger (Mighty Morphin Power Rangers) Soleil (Animal Crossing) Zhang Chuchu (Vocaloid)

[vocaloidfactoftheday]

All About Private Vocaloids!

a while ago i made a post about canceled vocaloids. this is a sort of sequel post about some private vocaloids. often these vocaloids are made and given only to exclusive users, eventually expiring or being retired. when it comes to abandoned vocaloids, private vocaloids and vocaloids that were never completed/released tend to be grouped together.

all information and images are from the vocaloid wiki unless stated otherwise.

the vocaloids discussed in this post are:

Kobayashi Matcha and Masaoka Azuki

Akikoloid-chan

Ueki-loid

Anri Rune

hide

Zhang Chuchu and Yuecheng

Kobayashi Matcha and Masaoka Azuki

Engine: VOCALOID2

Language: Japanese

Company: SEGA in collaboration with YAMAHA

Status: Retired; replaced with public VOCALOID4 voicebanks

They were made for use in Project 575, a multimedia franchise including several games and a series of 5 short anime episodes.

Specifically, the games Utayomi 575 and Uta Kumi 575 used the VOCALOID2 engine to synthesize their vocals in real time.

Their private use made them notable targets of piracy. Their voicebanks were cracked by extracting the voicebank data from Utayomi 575 and making them usable for VOCALOID2.

In 2017, they received VOCALOID4 updates and are now commercially available under the names MATCHA and AZUKI. These are the first private vocals to be made public, although their original VOCALOID2 voicebanks are still legally unobtainable.

Akikoloid-chan

Engine: VOCALOID3

Language: Japanese

Company: Lawson, Inc.

Status: Retired

Akikoloid-chan is a VOCALOID derivative of Akiko-chan, the new mascot for Lawson in 2011.

Lawson was the only group with the license to use her. She was used as a mascot on the Lawson Nico Nico Douga channel to help promote Lawson.

Akikoloid-chan is no longer used for music. However, Lawson still owns her character.

The mascot she originated from, Akiko-chan, is now a VTuber on the Lawson YouTube channel.

Ueki-loid

Engine: VOCALOID3

Language: Japanese

Company: Music Airport Inc.

Status: Unknown; possibly abandoned

This is the first use of VOCALOID that is an attempt to "revive" a deceased singer by emulating their vocals. The singer in question is Hitoshi Ueki, who passed away in 2007.

The voicebank was made by first recording the vocals of Hitoshi Ueki's oldest son, Hiro Kouichi. Then, they studied the differences between his and his father's voice, and edited the recordings to match Hitoshi Ueki's tone.

Only a few demos have been made for Ueki-loid. An album of songs was planned, but has never been released and has received no news since 2012.

It's possible that Ueki-loid was simply a test for the technology of reviving deceased singers' voices.

Anri Rune

Engine: VOCALOID3

Language: Japanese

Company: Fuji TV

Status: Retired

Anri Rune was originally a newscaster character on Fuji TV. Her career as a newscaster was short-lived, as she failed to bring in enough ratings to satisfy the network, and so she was retired.

In 2013, Anri Rune was repurposed as a VOCALOID mascot. She was given a new appearance and biographical information. She was also given a demo song by producer Kikuo, titled Hallelujah Super Idol.

After her single demo, there was no news on Rune's status. In response to a fan, Kikuo did not know her current status. YAMAHA also stated that Rune was never intended as a commercial product.

In May 2015, about 2 years after her debut, Fuji TV confirmed that Rune was retired and no products with her were planned.

"hide"

Engine: VOCALOID3

Language: Japanese

Company: Headwax Organization Co., Ltd.

Status: Unknown

Another attempt to "revive" a deceased singer, this time basing the vocal off of Matsumoto Hideto, better known as hide, the lead guitarist of rock band X-Japan who passed away in 1998.

Unlike Ueki-loid, samples were taken directly from recordings of hide's voice, not from any relatives.

The voicebank was used in the album Co Gal, which began production in 1998. hide was unable to complete the album before his death; the VOCALOID voicebank was used to complete the album with his voice.

The album was then released in December 2014. Fans remarked that the voice was unmistakably reminiscent of hide.

Due to using hide's voice, he is credited as the singer of all the songs in the Co Gal album despite the use of VOCALOID. Because of this, the actual name of the software itself is unknown, and is usually simply also referred to as "hide."

The status of this voicebank is unknown; it has not had any known usage since Co Gal.

Zhang Chuchu and Yuecheng (King)

(sorry chuchu for the crop)

Engine: VOCALOID4

Language: Chinese

Company: Shanghai Wangcheng

Status: Possibly abandoned

Chuchu and Yuecheng both originally appeared as characters in manhua. Chuchu was owned by Migu Comics while Yuecheng was owned by NetEase comics.

Shanghai Wangcheng, who had also worked with Gynoid on Xin Hua, wanted to create business-to-business VOCALOID voicebanks for private use, to turn preexisting characters into virtual idols, and approached these companies with the offer to develop VOCALOID4 voicebanks for their characters.

Both VOCALOIDs' voice providers were contest winners. The voicebanks were complete by April 30, 2018 and continued to be used by their authorized users.

In June 2020, an authorized user of these voicebanks reported that their serial codes had ceased distribution. YAMAHA had ended contracts with Shanghai Wangcheng, and Shanghai Wangcheng has since ceased activity.

This is a similar situation to the discontinuation of Utatane Piko. This does not stop authorized users from using these voicebanks, but no new users can gain access, and if switching computers, they can only be used for a 2 week trial period.

i hope you found this interesting! thanks!

[varian-polis]

Geez Polis how many of these did you finish but not post

This is the last one for now, I swear