Загрузка видео...

Не удалось загрузить видео

На главную

This new AI tool is interesting! ReconX can create a 3D scene using just two images from different angles. Imagine if we could train our character with only 2 images instead of 10?

69,436 просмотров • 1 год назад •via X (Twitter)

Комментарии: 10

Фото профиля el.cine
el.cine1 год назад

Paper:

Фото профиля TomLikesRobots🤖
TomLikesRobots🤖1 год назад

Interesting that it's not Google but they utilise Imagen and Phenaki. Unfortunately, it might also mean we won't get to play with it. This looks like a really interesting tool.

Фото профиля Hungry Donk-E
Hungry Donk-E1 год назад

🥕Wow! This is awesome! I recently tried something similar here using i2v

Фото профиля MayorkingAI
MayorkingAI1 год назад

very interesting!

Фото профиля el.cine
el.cine1 год назад

👍👍

Фото профиля Ivan P.
Ivan P.1 год назад

@SmagaAlex, we should explain how @Openmagic_ai functions in a similar manner.

Фото профиля Michael Ten 🌨🎶🫐🍀
Michael Ten 🌨🎶🫐🍀1 год назад

That's pretty cool. Machine learning sure is advancing quickly

Фото профиля el.cine
el.cine1 год назад

It’s super quick mate

Фото профиля andwhynut
andwhynut1 год назад

pinta fresco este reconX, Thx for sharing!!! have u seen the new vidu?

Фото профиля Teodora P L
Teodora P L1 год назад

Ooo wow this is going to be killer for Teal Estate

Похожие видео

📢📢 𝐀𝐯𝐚𝐭𝟑𝐫 📢📢 Avat3r creates high-quality 3D head avatars from just a few input images in a single forward pass with a new dynamic 3DGS reconstruction model. Video: Project: Our core idea is to make Gaussian Reconstruction Models animatable. We find that a simple cross-attention to an expression code sequence is already sufficient to model complex facial expressions. We then incorporate position maps from DUSt3R and feature maps from Sapiens to facilitate the prediction task. While DUSt3R's position maps act as a pixel-aligned initialization for the Gaussians' positions, the Sapiens feature maps help the cross-view transformer to match corresponding image tokens in the 4 input images. One major challenge in creating a 3D head avatar from smartphone images comes from inconsistent facial expressions when the subject could not remain perfectly static during the capture. We eliminate this static requirement by simply showing our model input images with different facial expressions during training. This technique makes our model robust to inconsistent input images later on. Finally, we show that despite the model has been trained with 4 input images, one can even create a 3D head avatar when only a single image is available. To achieve this, we employ a pre-trained 3D GAN to lift the single image to 3D and then render the 4 input images for our model. This allows us to create 3D head avatars from single images and even highly out-of-distribution examples like AI generated faces, paintings or statues. Great work by Tobias Kirschstein from his internship at Meta with Javier Romero, Artem Sevastopolsky, and Shunsuke Saito

Matthias Niessner

74,698 просмотров • 1 год назад