Video wird geladen...

Video konnte nicht geladen werden

Zur Startseite

Here are some tips for those who want to practice face acting: ➡️U motion: Move your head in a "U" Shape so it's never stuck in the same place. This will help your movements look more interesting. ➡️Key points: When practicing with your model, learn to get to a...

94,422 Aufrufe • vor 11 Monaten •via X (Twitter)

12 Kommentare

Profilbild von ♥️Cutiepierin | L2D RIG ARTIST
♥️Cutiepierin | L2D RIG ARTISTvor 11 Monaten

If you ever want a overally annoying in-depth video about everything I know- Lmk. XD I would love to annoy you about face tracking and parameter setup.

Profilbild von ♥️Cutiepierin | L2D RIG ARTIST
♥️Cutiepierin | L2D RIG ARTISTvor 11 Monaten

I’m a rigging artist and streamer if you ever wanna check me out :3

Profilbild von Bug 🔌🍀
Bug 🔌🍀vor 11 Monaten

thank you for sharing!!! i feel especially the key points you mentioned is something a lot of vtubers overlook - messing around with different expressions and seeing how they look on the model is super important! 🙏💚

Profilbild von Mith.io 🍃🐾🌼
Mith.io 🍃🐾🌼vor 11 Monaten

Thank you so much for sharing!!! It seems like common knowledge or common sense to those of us who did performance arts or anything in school, but so many folks don't realize that it's the motion that makes a model work as the creators intended!!!

Profilbild von ♥️Cutiepierin | L2D RIG ARTIST
♥️Cutiepierin | L2D RIG ARTISTvor 11 Monaten

Yeah it’s animation fundamentals when it comes down to it

Profilbild von Pyggy Brutus 🪨statue vtuber - debut wip
Pyggy Brutus 🪨statue vtuber - debut wipvor 11 Monaten

This reminds me that I need to look up how to actually use vbridger

Profilbild von TheVNeon28🤖💚
TheVNeon28🤖💚vor 11 Monaten

saving this for later so i can practice lol

Profilbild von SpaughtFire 💜PotatoPups💜
SpaughtFire 💜PotatoPups💜vor 11 Monaten

One of the dopest things to show up on my TL, tysm!

Profilbild von Lord Draconalis
Lord Draconalisvor 11 Monaten

I may or may not know someone in need of these tips in the relative near future. Thank you so much. Bookmarked for when the time hypothetically comes.

Profilbild von Supress.Vh
Supress.Vhvor 11 Monaten

Im using vroid rather than the other main ones, with i dont quite like since it isnt as expressive and has "set" expressions Im also having an issue with lighting but having a bright light in my face wouldnt be fun

Profilbild von Lag ☽ ⚔︎ тнє мσв gяιη∂ιηg νтυвєя ⚔︎ ☾
Lag ☽ ⚔︎ тнє мσв gяιη∂ιηg νтυвєя ⚔︎ ☾vor 11 Monaten

*taking more notes*

Profilbild von MalaFama | Live2d training arc ❌🤍
MalaFama | Live2d training arc ❌🤍vor 11 Monaten

the U motion can also be done with physics on live2d!

Ähnliche Videos

PROPHETIC DECLARATIONS In the name of Jesus Christ, I decree you're on your way to better days! Let this week come with pleasant surprises! I bless you this week! Let it be well with you! I decree: everything turns to joy for you this week! Everything turns for your peace! Everything turns for your lifting! Laughter is your portion! Restoration is your portion! A new level is your portion! Your world will celebrate you in a marvelous way this week, in the name of Jesus Christ! I declare: your prayer life jacks back to life! Your Word-study life will be on fire! I release my faith with you. Because you have left everything to be here, trusting God, may your testimony arrive speedily! Helpers come in a hurry! In the name of Jesus Christ! In the name of Jesus Christ, receive wisdom for the ember months! Wisdom for the remaining months of the year! What you could not achieve from January to August, I declare that you will achieve it in this month! In one month, you will achieve what you could not achieve in one year! You will surprise yourself this week! All who know you for good will recognize you this week! I pray for you: Your ministry blessed! Family blessed! The works of your hands blessed! Your going out is blessed and your coming in is blessed! Troublemakers will never get to you! Evildoers will never get to you! Plotters of evil will never get to you! Only goodness and mercy will get to you this week, in the name of Jesus Christ! I call you a child of God. Any blessing that follows a child of God will rain upon you! I declare this week: The earth will not swallow you! You will not be a victim of accident! You will not be a victim of tragedy, in the name of Jesus Christ! In the name of Jesus Christ, I bless your hands; experience the anointing! I release you as an ambassador this week! I declare this week: Go and heal the sick! You will only go higher and higher! You will only go upward and forward, in Jesus Christ's Name! #ApostleJoshuaSelman #CapacityAndStature #TheMysteryOfStatureAndDominion #KoinoniaAbuja #KoinoniaZaria #KoinoniaGlobal

Koinonia Global

21,377 Aufrufe • vor 1 Monat

How can you solve complex tasks using a Large Language Model? Here is a 2-minute introduction to everything you need to know to 10x the quality of your results. Let's talk about three techniques, in order of complexity, starting with the easiest one: • In-Context Learning • Indexing + In-Context Learning • Fine-tuning In-Context Learning The team that trained GPT-3 found something they couldn't explain: You can condition a model using examples of how you want it to behave. I included an example prompt in the attached video. You can "teach" the model how you want it to interpret questions, select the correct answers, and format the results by giving a few examples. You can also give specific knowledge to the model that will be helpful when formulating answers. We call this approach "grounding the model." There's another example in the video. Indexing + In-Context Learning Unfortunately, there is a limit to how much data you can include in a prompt. We call this the "context size." One version of GPT-4 supports a context of approximately 6,000 words, while the other supports 25,000 words. Although this sounds like a lot, many applications need more than that. Imagine you wrote a book and want to build an application to answer any questions about your story. What happens if your book is longer than the context? That's where Indexing comes in. Using a model, you can turn every book passage into an embedding. These are vectors, numbers that "encode" the passage's text. You can then store these embeddings in a particular database that supports fast retrieval of these vectors. You can then turn any question into an embedding and search the database for the list of passages that are similar to that query. Instead of using the entire book to ask the model, you can now use the relevant passages as in-context information, effectively working around the context size limitation. Fine-tuning Fine-tuning can give you an extra boost to get reliable outputs from your LLM. It is, however, the most complex approach on the list. There are different approaches to fine-tuning a model with your data. A popular technique is to process your data with your LLM and use the outputs to train a new classifier that solves your specific task. Notice that here you aren't modifying the LLM. Instead, you are chaining it with your trained classifier. Another approach is to modify the parameters of the LLM using your data. Think of this as "rewiring" the model in a way that solves your particular task. The results and costs will vary depending on how many layers you want to fine-tune from the original model. Many companies think that fine-tuning is the solution to their problems. In my experience, many will benefit from exploring the other two approaches. I love explaining Machine Learning and Artificial Intelligence ideas. If you enjoy in-depth content like this, follow me Santiago so you don't miss what comes next.

Santiago

384,573 Aufrufe • vor 3 Jahren

All these demo videos make HEAD SWAPPING with Nano Banana look so easy, but then you give it a try and you're like... uh... what? Why didn't that work? Here's what I've found. Nano Banana reads your image, almost literally, so if you write on the image, it reads the text. This is how Higgsfield AI 🧩 has capitalized on the tech: "Write on the image" and give it direction, right? Totally true, but you don't need Higgi to write on your image. Nano Banana will understand your direction regardless of where you write on your image. On one hand, Higgi is really smart, because they're hranessing the tech in a unique way, but the whole "Higgsfield's Banana Placement" is a bit of a misnomer. It's more of a "Banana Placement" and Higgi is just giving you a sort of basic Photoshop-type tool to work with (again, pretty smart), but the real tech is the Banana. 🍌 This is how I head swapped heads in Runway, but Nano Banana maintains the aesthetic qualities of your image almost perfectly, whereas Runway Reference spits out a very Gen-4 looking image. I like using Nano in Freepik (now Magnific), mainly because it's fast and I can get 4 gens at a time, and you need to gen a dozen times of so before you get a winner (most of the time). I was pumped when I saw Freepik introduce the @ reference feature, just like Runway has, but it doesn't seem to work for head swapping. My guess is because that's not really how Nano Banana tech works... ideally. Marco is the person I saw using this "A" and "B" method, back when Nano was on LM Arena, and man-oh-man, it just works... like a charm. You need experiment with how much of the face you blot out, and the angle and facial expression of your new head if you want the blend to be perfect. All of the results in this video are 100% Nano Banana. I did not do any Photoshop work to the images after the fact. I really hope this helps. Let me know if you have any questions. I'm happy to help. And I'll keep posting videos like this if you guys find them useful. Let me know! And if you want more serious, one-on-one AI consultation you can throw something on the books here:

Jordan Daniel Chesney

62,235 Aufrufe • vor 1 Jahr