How 3D & AI Could Kill the Grade for Good | FIT | PI Apparel

Hello everyone and welcome to another Seamless Conversation. Today I'm excited to be joined by Lee Lavange, a Junk Professor at FIT, who is leading the work behind the winning academic submission for the 3DRC Grand Challenge 2025. Lee's proposal on revolutionizing product development with data-driven AI and 3D innovation aims to tackle one of industry's biggest hurdles: how to move beyond standard pattern grading to a future where every customer becomes their own base size, enabling smarter design, better fit, and less waste. So with that said, welcome Lee, and a huge congratulations on your win.

Thank you so much. I'm so excited to have this conversation.

Absolutely. And you're just back from Pye Apparel New York. Again, I'm really, really sorry to have missed the event and to have missed you there, uh, but it was announced that you'd won. How did that feel at the time, in the moment? Was it great?

My heart was beating very fast. I don't like eyes on me, but, um, it felt very This whole thing has felt very validating, very exciting. You know, you sit with your ideas for a long, long time and then suddenly you have a moment where you get to share the ideas. Other people are like, that's a great idea. It feels really nice. I was— I met a lot of people the first day at PI that I didn't know, industry people, you know, technology people. So it was fun then the second day after it was announced to meet up with them and they were like, wait, you won an award? And then share with them my idea. So it was a really fun experience.

Amazing. I'm so glad to hear that. And as I say, I'm only sorry that I missed it, but really glad that we get an opportunity to chat more about it today. So look, obviously everyone knows who FITT is, but for anyone who doesn't know you, could you start by giving us a little bit of an introduction as to who you are and your background that has led you up to this moment?

Absolutely. So I'm Lila Vange, as you said. I live in New York City. I currently teach at FIT. I'll be starting full-time teaching in the fall, which I'm very excited about. And I'm in the tech design department, which is a small department within the school, but we're growing fast. So this is really exciting. Before I started teaching, I was in the industry. I worked as a tech designer. I then moved into pattern making, and I actually went back to FIT at some point during my career to get— do a certificate program to, you know, enhance my pattern making and grading knowledge. Um, about midway, you know, through, I started at, um, VF at the time, turned into Contour Brands. Um, and that is where I think I, um, really kind of stepped up my career trajectory. Um, I— we were pattern makers. Um, we did our own patterns, our own blocks, our own production patterns. And grading. And so that's where I really kind of honed in on grading and realized that I really enjoy that mathematical side of pattern making. It's also where I learned 3D, um, and we were early adopters at the time of 3D technology, which was cool. Um, and so I think being in it so early and kind of having the freedom to play with it, um, allowed me to think outside the box. I think people who are starting in 3D today, there's this prescribed path. You do this, you do a pilot program, da da da da da. And we got to build that as we went. And so I think that kind of set me on a path to really challenge what we can do with technology in fashion. So fast Fast forward to today, I teach computerized pattern making at FIT, and I teach computerized grading, and I teach 3D. So I get to kind of put the whole process together for my students. And I think just being in that and kind of having to take your knowledge and give it to someone else, it also makes you question, you know, why do we do this? Why are— why have we never changed our methods of how we do this? And the fact of the matter is our human bodies are changing. The way we shop, the way customers buy product is changing. Yet we haven't really changed how we make clothes.

Yeah, absolutely. Well, thank you for that. And yeah, it's amazing too that you've come from industry and are now teaching in academia as well. I think that there's a big need to kind of bridge the gap as much as we can. For students that are going to be going on into the industry, especially as things continue to move so fast. So I think that's amazing. Um, I gave a very, very brief summary at the beginning of this about what your, uh, submission for the 3DRC Grand Challenge was all about, but if you could dive into that a little bit more, I'm sure our readers would love to understand it.

Yeah, absolutely. Um, so the proposal I submitted, um, you know, in simple terms is is a way to customize fit for any body type, any customer that comes, you know, shopping on your website. So the way I see it is you're a brand, you have a particular design that you have created and fit in 3D on your fit avatar, right? That's your sort of guidepost, your true north. Then when it goes to the customer, right? So let's say you've made no inventory, you've put this 3D asset up on a website, then the customer shops, they say, oh, I really like this. Then they can do a body scan with their iPhone. There's lots of body scanning apps these days, and there is an integration between AI and 3D. So That data that's input by the customer is then fed into 3D to first adjust the avatar. So this customer now has a custom avatar that is their body measurements and shape. And then those same deltas between the customer avatar and the fit avatar are applied to the pattern. So the way I kind of describe it is it's— we're not grading. We're taking the theory behind how we grade. And we're essentially adjusting the pattern. So just like if you were to go into a tailor's shop and say, I want a custom suit made, they're going to measure you, they're going to pick your, you know, what kind of lapels you want and what fit pant you want. And they're going to make a pattern that's just for you. And so that's essentially what I'm, I'm proposing here, but it's automated.

I love that. As someone myself who does not really online shop, especially for tops because of this exact same thing, like I have to go in and try and try things on and there's so much disparity between sizes and grades between different brands. Like I think this sounds amazing. So big fan as well, just from a personal standpoint too. And you know, I hope, I hope it doesn't take long for brands to start to adopt this as you develop it more. And I mean, I probably am going to know the answer to this, but why focus on grading and fit? You know, was there a specific moment or insight from your industry experience that really inspired you to think, you know, this is a critical problem that I want to solve?

Um, I think not a moment, but a culmination of a lot of, um, moments throughout my career so far. Um, you know, grading, grading comes from the human body, right? So there are, um, there are companies out there that collect body data, and they, you know, sell and create charts with all sorts of body, you know, sizing data measurements for every single point around your body. These are averages. So when you take hundreds and thousands of average body measurements, and create one size in the middle, oftentimes we're creating a body shape that isn't realistic. Um, so, you know, there are tons of different body shapes. You could have a straighter shape, you could have a curvier shape. There's a million different body, body types in the middle as well. Um, and so averaging, yes, is a nice way to kind of find a middle point. Um, but at the end of the day, we're we're kind of the customer sacrificing. They're, they're not ever getting something that really truly fits them. So, you know, you talk to people and they say, well, I wear this size in this brand, this size in this brand, this one, I just buy this size, but I have to wear a belt around the waist because my, my hips are curvier and my waist is smaller, right? We make all these, these concessions as we shop. Because we're, we're we're working in this world of mass production. So I think just that realization that these are all averaged numbers, averaged body types, so therefore no one's really winning. I think that, and then, you know, just being in the industry, I've watched a lot of brands try to launch, you know, extended sizing or Yeah, extended sizing is a good example. Because plus sizing means a new fit, a new size scale, a different set of SKUs, right? It's more money, it's more inventory, it's more risk. And so they said, well, let's just, let's just extend the size range we already have, that's more cost efficient. It's, it's never been super successful because the plus body is a different skeleton. It's a different shape, the proportions are different. Plus-size bodies grow in different areas than, say, a Missy-size body or a petite-size body. You can't grade from a size 6 to a size 26 proportionally and expect that to fit well. So it's not successful because the customer's not there. There's a— there is a customer base for plus It's because we're not doing it correctly. We're not sizing these garments correctly. So I think as I've gone through my career, I've just sort of seen these inconsistencies and these just holes where I think we can do better than this.

Yeah. Amazing. I love that. And obviously, yeah, you know, with the rapidly evolving landscape of AI, it's amazing now that we can leverage it in that kind of way and integrate integrate it with the 3D assets that majority of brands are already making now anyway. I do remember in the submission that you, um, uh, sent in that you had mentioned, uh, the importance of fit pressure, um, in this field, um, and how garments really interact with the body. How are you capturing and interpreting, uh, that kind of data in the work that you're doing?

Yeah, so I mean, I know most, most tech designers and pattern makers that are in 3D today know, know what I'm talking about when I say pressure and tension maps, But essentially, when you dress the body on the garment on the avatar, you can put these maps on. It looks like a heat map, right? So you can see where the garment has the highest pressure or is just touching the body or is not touching at all. And then with the tension, has more to do with the fabric, right? Where is the fabric being, being pulled the most, being stretched the most? So that brings in the fabric component as well. And we use these as visual cues today, right? So I'll fit something, I open up the pressure map, and I say, okay, this looks consistent to this, or, um, there's red around the waist, this is too tight, right? So we're making these subjective decisions with this color map. But to build the color map, there has to be math, there has to be data, right? So there's something happening behind the scenes, um, that's producing this color map. And so If you look into it, there's, there is a mathematical point at every point on the grid, the 3D mesh, right, of the garment that tells you how much pressure is at that point and how much tension is at that point. So being able to, to put an actual data point versus us using our eyes and subjectively deciding this looks loose, this looks tight, is, is a game changer for this, right? Up until this point, we sit in a fit session, you have a fit model, you have a table full of designers and tech designers, the fit model puts it on, and then she tells you, the waist feels too tight when I walk, the thigh is rubbing, or, you know, and then you see things, the inseam looks too short, right? So these are all things we're observing. But how do we quantify. And so the pressure map and the tension map give us a tool in 3D to quantify that. It's as if the avatar is telling us, this chest is way too tight, right? Rather than seeing that the space between the buttons is pulling open, and that tells us it's too tight, we actually have this data behind it.

Yeah, amazing. A lot more specific. I also would love to talk about, and you mentioned it a moment ago, obviously we live in an industry at the moment that is very, very much on the mass production side. And so moving from that to a world where every customer is their own base size is a massive paradigm shift. So I wondered, how do you actually envisage the industry preserving design intent while adopting this work and adopting patterns for individual bodies?

Yeah, I imagine designers and tech designers listening to this interview, like, shaking in their boots, you know, like this. No, no, it's too scary that we can't do this. We're going to lose our— the integrity of the design. I actually— this is like a big point in my brain as I've been, you know, writing this out and developing this idea is I think that original design is so important. There are plenty of companies that are, you know, saying, come and design your own thing in our platform. I'm of the belief that I don't think every customer wants to design their own thing. I think they like to be told what's, what's on trend, what's new. What do you— you're an expert designer, what do you think is going to look good on me this season? And so I think that's very important that once they've designed that product, you know, and we start customizing, are we, are we turning it in this garment into something that the designer doesn't think looks good or isn't correct? And so the way I envision the, you know, the in the weeds of this working is if you start with grading theory, you're going to start with a default understanding that you're growing and shrinking proportionally. Where that changes is, let's say you have a customer who— back to my straight versus curvy body type, right? That's a very standard example. You've built a jean that is for a very straight body. A customer comes in, and let's say from the fit avatar to the customer, the waist grows an inch, but the hip grows 2 inches. Right? So if the customer were just to pick the next size up, the hip might be too tight, but the waist fits. If she goes 2 sizes up, the hip fits, but now her waist is gaping. And so making that small adjustment where we're growing certain areas a little bit more or a little bit less is a simple pattern correction. It's not— we're not changing the, the look of the garment. The shape of the leg stays the same, right? All of those other points— so say, let's say it's a flare leg the proportion between the knee and the leg bottom, that's going to grow proportionally still, right? You don't get to say, oh, I want to turn this into a skinny jean. It's really just about tweaking those minor things that are going to help it fit your body shape better. So yes, maintaining that original fit intent, I think, is, is incredibly important.

Yeah, absolutely. And you spoke before about kind of like shirts and buttons and understanding the pressure and fit there. You've now just spoken about jeans. In your mind, does the work that you're doing apply really to any kind of apparel, or are there particular kinds that you think are more ready to be adapted in this way?

My goal is this works for anything. I don't— if you listen, if you go to these conferences like PI and you listen to these technology companies, you can tell they have a specific garment type that they do really well, right? Or let's say your customized t-shirts, right? Like, because they've made it efficient that they can create the same t-shirt over and over again. Or, you know, customization of like a men's shirt— change the collar, change the cuff, change the placket size— those are, you know, easy components to adjust. I want a designer to submit an asymmetrical ruched dress with a flared skirt and be able to customize that size. That is my ultimate goal. And I realize that sounds crazy, but it's a challenge. But the same— I teach this to my students, this though, we teach them to grade a bodice a skirt, a pant, right? We don't go into crazy different designs because the concept that you use to grade a bodice, you use to grade any top, right? There are certain things, a dolman sleeve or a kimono or something where we might do something a little bit different. And we go, you know, we train on those things as well. But the underlying method is the same no matter what the garment looks like.

Yeah, absolutely. And look, I think, you know, challenging the issue of fit is such a huge area. I think ever since we launched Py Apparel back in 2013, fit has always been one of these things that, you know, it's always mentioned in the research, but then not a lot of people want to actually talk about it at events because no one really knows, you know, how to solve it. It's also such a personal and subjective experience for each consumer that is buying things. How do you foresee your solution scaling that kind of level of personalization while still being technically and commercially viable?

Yeah, for sure. So I think one of the— and probably any patternmaker you talk to about this idea, the first thing they'll ask you is, but how is this garment sewable when it comes out of the other side, right? So okay, maybe we can automate pattern corrections or grading. But at the end of the day, every, every patternmaker who grades on the computer knows, after you've graded, you have to check everything, right? Does your sleeve cap still match your armhole? Do your side seams still match? Do you know, did— is the balance still there, etc.? So that is part of this, right? We're not— again, we're not changing things in disproportional way. But I also recognize that even after we've adjusted a pattern, we still have to understand, is this going to be sewable? And so that, that is, that'll be the kind of final key throughout this process to link this 3D world to the manufacturing world. And that's a— I talk about that in my proposal a bit, that that's one of the big challenges is We've seen this with 3D already. We can't just change one section of the product development process. We have to change every single part from design to production because there has to be this interconnectivity. So I do think there is a model of manufacturing that works for this type of customization and, you know, making one garnet at a time. And we have to make sure that that output coming from 3D is sewable, all of the data is there, etc. So that's, that is one of the biggest components, I think, to making sure that this model is scalable.

Nice. Yeah. And I mean, we spoke a little bit about design and tech design and now also production too. But I mean, as far as I see it, this should also have kind of like far reach reaching impacts on things like retail returns and sustainability too? Like, how do you see it playing out in the wider value chain beyond production?

Oh, yeah. I mean, I think huge impacts, right? I mean, we've been talking about sustainability from all, all ends of the spectrum, right, from fabric all the way through to production. But we all know, and we all saw this during COVID that inventory is one of our one of our biggest wastes in this industry, right? So being able to sell something and then make it and ship it and not have to take the risk of what sizes are people going to buy, how many, is this going to be a popular style or not, we remove that risk of inventory and then the ultimate waste of inventory as well. I think also, you know, fit is— most would argue fit is the number one reason for customer returns. And I think that it would be a huge unlock for customers to know and to actually visualize themselves. We've been talking about virtual fit rooms for a long time. So far, it's been kind of on the surface and not a real representation. I think for a customer to truly see their body shape with a garment will, will be huge. We've seen like, you know, you go shopping online, you see the size guides, you see the extra information. This is the inseam length, this is the waist. You see now some brands will put images of, of their garment on different body types, right? So they can see, maybe you'll see yourself in one of these people. Or you see the ones where customers can put images of themselves in the garment so that you can see other people wearing it, right? All these things are to help customers buy what they, they like and that will fit them and not return it. But to me, they're all Band-Aids, right? They're helpful tools, sure. But we have to have those because we don't fit the customer perfectly. And so until we do, we're going to continue to have returns.

Of course. And also like, not only, you know, is the work that you're doing going to help with that transaction, but also like from a, from a consumer loyalty standpoint, I mean, if I knew that there was a particular brand that had adopted this kind of tech and had this particular model, and I could go to their, to their site and know that I could get stuff made specifically for my sizing. I mean, it would be, it would be huge from a consumer loyalty standpoint too. And no doubt they continue to want to come back, which is amazing. So the next thing, Leigh, that I'd love to ask is what were or are still some of the most difficult challenges that you faced or are facing in combining grading science with both 3D and AI?

Yeah, a few things. So one, I will say most pattern and grading knowledge today is inside of people's heads. You go to a company and, you know, I hear this from my students a lot. We just want to, we want to learn, we want to learn. You have to have good mentors. You learn a lot in school, but there's only so many hours and semesters that we have to teach. So I think just, you know, capturing that into the technology, right? So that knowledge that, you know, oh, well, if you just tweak this little thing here, it actually really helps with the shoulder slope fit. Those little pieces of knowledge that aren't in a book, they're not necessarily online. Maybe someone has a YouTube video about it. They— that, that's— those little pieces of information are going to be very key. AI, we all know AI works with data, right? AI needs data to be able to work. So just having enough data to feed and, and make sure we have the right decision points, right? So, yes, there are rules that we follow with patternmaking, but all patternmakers make decisions as they go. Okay, if I'm going to drop the armhole, do I then need to adjust the slope? Do I need to adjust the the sleeve cap. So there are, there are decisions that happen all the way through that process of pattern making and grading. And those are things that we're going to have to feed into the system so that, you know, AI can do its job. Lastly, I will say, we are currently constrained by the functionality of today's softwares. So what I can do in 3D is what I can do. And what I can automate. And there are things, you know, as I work in it every day, there are tiny things as a pattern maker that maybe could be improved or that take a little extra steps to kind of work around. So there's definitely some constraints there that, that, you know, we have to figure out how to work around.

Absolutely. So tech vendors, take note. There are things that you, that you can improve in your tools. I'm sure you already know. I'm sure you're working hard on all of those things. But, but yeah, I mean, I also wanted to ask you, I mean, you mentioned then about working with real-world data. And as you said, you know, AI is only good as the data that you put in. But what I also wanted to ask is, having worked with that data now, have there been any surprises about how garments actually behave on different kinds of bodies and shapes?

Yeah. You know, I wouldn't say surprises, but I do, you know, as I was mentioning before, the pressure maps and tension maps give us this kind of insider's knowledge of how something is, is fitting or changing or pressuring on the body. And I, I think, you know, having stared at these for years and years and, you know, worked through this process, there are things that, that you get from them that you don't get from a regular fit session. And sometimes it's not the pressure, sometimes it's the lack of pressure, right? So take a shoulder slope. That's like my favorite, my favorite fit. My favorite fit problem is a shoulder slope problem, right? So maybe your shoulder's too sloped or your sleeve cap's too long. And when a fit model stands perfectly still, she looks great. Um, until she starts moving her arms, can you maybe tell there's a little bit of an issue? But in 3D, when she's standing still, you see pressure here on the top of the shoulder, and then what you'll see is a gap. You'll see it like the— a little, you know, no pressure right here at the neck. Um, and, and then if you try to move her arms, then you'll start to see, you know, the, the fabric pulling and etc. But you can see it before she even starts moving, and that's something that not necessarily you could see in, in person. And so I think those little things, just really honing in on how much information we can gain from these pressure maps is huge.

Absolutely. And when you've been doing this work and been going along, how have you been validating your results? And I guess, what does success look like for you in the research context?

Yeah, I mean, little by little. You know, we talked earlier about how my ideal goal would be to be able to make any type of— customize any type of garment or pattern that comes into the system. But starting at a specific category, and then adding in variables like fabric. So let's take like a blouse, like a bodice, right? So perfecting the bodice, any, any, we can grow the bodice from any size to any size. And, and understand this kind of hierarchy of critical fit points, right. So on a bodice, mostly for a woman, our number one critical fit point is going to be the bust. Second to that will probably be the waist, then we might move into the arm and the bicep, right. So kind of creating those hierarchies for each product category is kind of how we're going through this. And then you add in fabrication, right? So how we make a decision about grading or scaling a woven blouse is going to be different than how we might scale a 100% cotton rib tank top, right? Because we have stretch on our side, and stretch allows us to do a little bit more and be a little more flexible. Possible. So, you know, working through product category by product category and then adding in variables such as fabric into that as well. You know, success would be, as I said before, to be able to take any design as an input, any body type as an input, and be able to successfully scale that to a new, to a new size.

Absolutely amazing. And I wanted to talk to you a little bit now about the 3DRC process itself. And again, you know, a reminder to everyone that Elise's work is the academic winner of the 3DRC Grand Challenge 2025. And in the write-up too, I'll be sure to include a link to what the actual challenge was and the specifics of it this year. I'd love to know what motivated you to take part in the 3DRC Grand challenge? And did the process help reshape your thinking or direction with your research at all?

Yeah, absolutely. You know, I have followed the 3DRC for a long time. When I was in industry, I went to, you know, these events like PI, and I see, I get the emails every year, there's a challenge. And so it's something that's always been in the back of my mind. I've mentioned at the beginning, I've always been a big ideas person. And, but, you know, it's a— you're a startup or academic, and I've always been an industry worker. And so there wasn't that opportunity for me to do that. However, I had opportunities in the industry to, you know, test out ideas as well. So when it came through, actually, my chair of the department forwarded it to all of us and I read the prompt and that's what did it for me. I was like, this is it. You're like, this is it. I have the answer. It was sort of this serendipitous moment of like, oh, I have to enter this. It's just something I feel so passionately about. So I was really excited to have the opportunity to try to get my ideas out there.

Absolutely. And we're glad that you did. As a founder of the 3DLC, but also I was a judge during the process too, and I will say this year we had an amazing lineup of submit— uh, submissions too, across both academic and startup. But again, yeah, yours was great, so I'm really, really pleased that you took part and that you could be at the event when it was announced too, uh, last year— um, last year, last week in New York. Um, yeah, I wanted to ask, during, during this process, be it specifically during the actual challenge process itself or while you've been working on this research, has there been a specific, um, a mentor or moment or piece of advice that has been given to you that has had like a significant impact on the, on the direction that you've taken?

Um, I can't say during this time at one specific moment. Um, I can say throughout my career I've had numerous mentors that I think have built me to the place I am today. Um, and, uh, The number one being my, my director when I was at VF/Contour Brands, Robert Garner. He was a— he taught me everything I know about pattern making and grading. I mean, I learned in school, you know, but he, he was just one of those really great leaders. And, you know, he really helped people shape and move where they felt comfortable or where they were leaning towards. And so he gave us that space, all of us. We had a team of 40 pattern makers, and he was also a huge champion of 3D. And so I just, I always go back to that. That felt like a very pivotal moment in my career to be there and have the opportunities I did and have the team and the leadership that I did. I also have a coworker who I met there, but we've worked together for many years after that on different projects and consulting. Her name is Caitlin, and she has been such an important thought partner to me. We are those types of friends where you can text any time of day and say, I have this crazy idea, and she'll ping pong back and forth with me. And I think having those people in your life especially when you're trying to do something new or different. Having someone who just gets it and doesn't question it, but just says, hmm, interesting, what if, right? Instead of, I don't know that that's going to work. I think that having those people in your life are extremely important.

100%. So shout out to both Robert and Caitlin for coming along on the journey with you. What does it mean for you and your team to win the challenge? And I guess, you know, what do you kind of hope that, uh, the kind of momentum will create for your work after having won?

Yeah, um, it's— it means so much to me. Um, I, you know, I, I think I mentioned that our tech design department at FIT is small but growing pretty quickly. Um, and so I, I do hope that it gives us a little, uh, puts us on the map a little bit and, um, you know, helps us recruit students that are not only interested in getting a tech design job in the industry but also hoping to challenge the way we do things, innovate around technology. Um, you know, I always say good pattern makers are true engineers and have, you know, math minds and engineering brains. So, you know, I do hope that we can bring in more students and just do more of these types of projects as well, more research, and really push the boundaries.

Absolutely. And in terms of what's next, you know, what are the plans to evolve this even more?

Yeah, I think, you know, growing a team that can continue this work. I know I've been talking about this in kind of vague and simpler terms, but the pattern part of this is extremely complex and is going to take time to fully develop to a product that could actually work in a— on a brand's website. And so, you know, I just hope that we can get support funding. I would love partnerships with brands or technology companies. I feel very strongly that we won't have change in this industry until we connect all of the pieces. We're still very, very siloed. So, so yeah.

Absolutely. Absolutely. I mean, we've, we've seen this industry be a silo since we launched, uh, but again, back in 2013, and slowly, slowly there have been improvements. But, uh, but yeah, 100%. And on the, um, on the topic of partnerships, I guess I'd love to ask you, what would an ideal industry, uh, you know, brand or technology partnership look like to you? Um, yeah. Please use this as a way to kind of do a call out, uh, please come and work with us.

Oh yeah, I won't call out any specific brands, but you know, I thought about this for a while and I feel like the obvious answer would be, you know, a big corporate company that has a lot of money and is already invested in all the technology. And, um, but I kind of changed my mind a little bit and I actually sort of feel like a smaller, maybe more agile brand could be a really good partner, right? Because I think about the corporations I've worked for and like just finding the person who puts the sizes on the website is like, it takes you a whole day, right, of emails. Do you know who this is? Do you know who this is? So I, I do feel like a smaller, more agile company, and also I sometimes feel it's these smaller brands are a little bit more willing to take risks because they have less, you know, less just constraints.

Bureaucracy and legacy.

Yeah, yeah, yeah. So I see something like that working really well, almost a sandbox in which we can test and play, I think could work really well. And then I mentioned before, you know, partnerships with not only 3D technology providers, but also there's a whole avatar component to this, right? So companies that are already working on parametric avatars where you can adjust, but making that automatic, right? So partnering with, with a company like that would be, would be really helpful.

Absolutely. I love that. Okay, well, look, let's end with this then. Um, you know, you, you've said, uh, a couple of times throughout this that you are an ideas person. You clearly are. Um, whether it's related to the Chat Challenge or more broadly, what advice would you have to other students or researchers or designers or anyone kind of working in the space that are kind of wanting to rethink the rules of product development through AI and 3D?

Yeah, I would say don't be afraid to speak your ideas. Fashion is like a little secret club, right? We like to keep our information in, we like to keep our secrets. We don't want anybody copying or taking. And I think though that until we start sharing, We're just going to keep doing things the way they've always been done. I've heard teachers and mentors say that, right? A young person will say, but why are we— why do you do this? Why are you changing that a half an inch? Or what, you know, and they'll say, well, that's just the standard, or that's just the way it's always been done, right? Anytime someone says that's the way it's always been done, I say, I call BS. No, tell me why. Because if we understand why, then we can question it, right? We can say, okay, well, okay, I understand why, I understand how. Now, is there a different way? And so I think just speaking the ideas, you know, finding those thought partners is super helpful. And then, you know, like I said, just, I think collaboration is going to be key. I really believe that if we can connect the dots, if we can all bring our wonderful innovations together, that's when we'll really change the industry.

Absolutely. Well, Leigh, thank you so, so much for your time today. It's been great to understand more about the work that you're doing. A huge congrats again to you and the team. As I mentioned, I will of course ensure that I I tag you in this article on Seamless. And to anyone who is watching this, whether you're from a brand, another academic institute, a technology company, if you think that you can contribute to this work in any way, please reach out to Leigh. And yeah, really excited to see what you do next. So thank you so much.

Thank you so much. Appreciate it.