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kfir aberman
เข้าร่วมเมื่อ 1 พ.ย. 2016
MyStyle: A Personalized Generative Prior
Project page: mystyle-personalized-prior.github.io
Paper: arxiv.org/abs/2203.17272
Yotam Nitzan, Kfir Aberman, Qiurui He, Orly Liba, Michal Yarom, Yossi Gandelsman, Inbar Mosseri, Yael Pritch, Daniel Cohen-Or.
Abstract:
We introduce MyStyle, a personalized deep generative prior trained with a few shots of an individual. MyStyle allows to reconstruct, enhance and edit images of a specific person, such that the output is faithful to the person's key facial characteristics.
Given a small reference set of portrait images of a person ($\sim 100$), we tune the weights of a pretrained StyleGAN face generator to form a local, low-dimensional, personalized manifold in the latent space.
We show that this manifold constitutes a personalized region that spans latent codes associated with diverse portrait images of the individual.
Moreover, we demonstrate that we obtain a personalized generative prior, and propose a unified approach to apply it
to various ill-posed image enhancement problems, such as inpainting and super-resolution, as well as semantic editing. Using the personalized generative prior we obtain outputs that exhibit high-fidelity to the input images and are also faithful to the key facial characteristics of the individual in the reference set.
We demonstrate our method with fair-use images of numerous widely recognizable individuals for whom we have the prior knowledge for a qualitative evaluation of the expected outcome.
We evaluate our approach against few-shots baselines and show that our personalized prior, quantitatively and qualitatively, outperforms state-of-the-art alternatives.
Paper: arxiv.org/abs/2203.17272
Yotam Nitzan, Kfir Aberman, Qiurui He, Orly Liba, Michal Yarom, Yossi Gandelsman, Inbar Mosseri, Yael Pritch, Daniel Cohen-Or.
Abstract:
We introduce MyStyle, a personalized deep generative prior trained with a few shots of an individual. MyStyle allows to reconstruct, enhance and edit images of a specific person, such that the output is faithful to the person's key facial characteristics.
Given a small reference set of portrait images of a person ($\sim 100$), we tune the weights of a pretrained StyleGAN face generator to form a local, low-dimensional, personalized manifold in the latent space.
We show that this manifold constitutes a personalized region that spans latent codes associated with diverse portrait images of the individual.
Moreover, we demonstrate that we obtain a personalized generative prior, and propose a unified approach to apply it
to various ill-posed image enhancement problems, such as inpainting and super-resolution, as well as semantic editing. Using the personalized generative prior we obtain outputs that exhibit high-fidelity to the input images and are also faithful to the key facial characteristics of the individual in the reference set.
We demonstrate our method with fair-use images of numerous widely recognizable individuals for whom we have the prior knowledge for a qualitative evaluation of the expected outcome.
We evaluate our approach against few-shots baselines and show that our personalized prior, quantitatively and qualitatively, outperforms state-of-the-art alternatives.
มุมมอง: 1 469
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Nice work! Keep it up!
Looks OP AF
Is there any comparison with XNect?
i am a animator and i am not coder how to use this program or how to install this please help me
Install Ubuntu either with dual system or virtual machine.Use anaconda to manage the environment...It’s not hard for non-coders,you just need to be familiar with this.
Nice Nice Nice!!!
Do you enable monetisation? Two ads at the start here regardless.
TH-cam place ads regardless now.
great work..
Thank you
Amazing!
Superb work guys!
Looks great! Will try it out soon, though I’ll probably need to wait until I get something better than a 1070 😉
I'm working on an idea that mixes fashion with AR. On this platform I need artificial intelligence skeleton deep learning. I think you can help me. Can u call me on Instagram??? @mazangrandi please
did they hire someone to do the narration
in this fu8ing t-pose))) nice work!
nice work
At 1:45 "Our skeletal convolution is done by a collection of temporal kernels whose support varies across the skeleton structure, and our skeletal pooling is performed by merging adjacent armatures." I can see the target audience for this video is super hyper knowledgeable experts in this field.
Yes, the paper was a tad confusing too... temporal kernels are the same as any old convolutional kernel but with one of the dimensions being time. Imagine a 2D pixel graph(black and white picture). If you wanted a black and white video you could stack these graphs on top of each other. Then you have a cube with one of the sides being time. A 3D kernel which convolves over this larger cube would then be a temporal kernel because it is convolving over time and the different images of the video sequence. "The support varies over skeleton structure" I think is because the skeletal graphs have to be homeomorphic which is a general math term but in the context of geometry it means two objects being able to be morphed into each other without breaking the object. Example, a Donut can be morphed into a coffee mug, but a sphere cannot without tearing the mesh. So a donut and a coffee mug are homeomorphic, a sphere is NOT homeomorphic to a donut. In this context, if you train the network to work on people, it will only work on people, 5 limbs coming from a center (neck, Larm Rarm, Rleg, Lleg). If you trained it on a worm, it will only work for worms. Finally, "merging adjacent armatures" was something I found terribly confusing. Mainly because "Armature" in the 3D modeling world typically means an entire skeleton. They are using it for just a bone segment. So they take two adjacent bones and merge them together in the skeleton. This operation preserves the skeleton form but also brings the skeleton into a more general space that can be decoded into other types of skeletons.
Thank you , would you please make new explaining from the beginning, please
The incredibly smooth voice almost feels like he's going to sell me this "one weird trick" for retargeting my motion vectors, call 800-NEURAL, first 100 to sign get a free pack of neurons 😂 Jokes aside, amazing work guys. Hope at some point this becomes some "retarget motion" checkbox in game engines and whatever dedicated motion capture software that was.
Where is the paper?
I look forward to reading it.
@@LudditeCyborg Here it is: arxiv.org/pdf/2006.12075v1.pdf
Great work!
This is truly wizard work. The idea of disentangling the models and reducing them to a common latent space is 🤯. Is there ANYTHING that a neural network won't optimize???
They can even optimize each-other. Google meta-learning !
People.
@@neo2652 not even; they can figure out better methods for us to perform tasks
amazing!
I just had a similar idea for a deep learning capstone project: to recognize human motion in a video (or a series of frames taken from a video) and transfer the motion to a 2d stick-figure demo, and then I remembered that I have seen something like this at the siggraph and found your work. I'll take a closer look at your website and paper tomorrow, but I was wondering if it's possible/feasible if I followed your example and created a simplified model for my project. I'm supposed to compare a couple different algorithms and demonstrate the difference in accuracy. I looked at other capstone submissions for inspiration and found mostly numeric prediction and sentiment or image-classification or digit/letter recognition use-cases. I was afraid that human motion and animating a stick-figure may far exceed the scope and my current skill-level. Do you think it's realistic to tackle this problem as a challenge for the next 1-3 months? Thank you!
Yeah... after having given it another thought I found that this is probably too advanced for me at this point. I probably should start with static images before I think about motion. But if you have any ideas for beginner-friendly deep learning projects in computer graphics, I'd love to hear about them! :)
Gerrit Wessendorf I would suggest you to use Random Forest to estimate a 2D skeleton (this method is used in Kinect v1). And you probably will need to rectify the frame before applying any algorithm.
@@okktok Thank you for the suggestion! When I searched for Random Forest I found some promising links to get started.
Where is the tool?
It looks incredible! Will this product be put up for sale?
superb! is code available somewhere on github?
It is on github: motionretargeting2d.github.io/
in another words magic
This screams meme!
Does this mean the previous methods are all obsolete?
What used to extract the 18 points skeleton model ? OpenPose ?
This work would be a best buddy for work like this: th-cam.com/video/WbJdfOPkKV8/w-d-xo.html&ab_channel=HuguesHoppe
Hi, That's true :). We use this framework to automatically generate image morphing effect. See the paper for more detials.