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🌟 Create anything in 3D! 🌟 Introducing CAT3D: a new method that generates high-fidelity 3D scenes from any number of real or generated images in one minute, powered by multi-view diffusion models. w/ lovely coauthors Aleksander Holynski, Ben Poole and an amazing team!

Ruiqi Gao

14,882 subscribers

153,307 просмотров • 2 лет назад •via X (Twitter)

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Фото профиля Ruiqi Gao

Ruiqi Gao2 лет назад

Website: Paper: Also, check out our gallery for hundreds of diverse generated 3D scenes!

Фото профиля Ruiqi Gao

Ruiqi Gao2 лет назад

CAT3D uses a multi-view diffusion model to synthesize a large set of consistent novel views from the input image(s). These views are then used to reconstruct a detailed 3D scene using robust reconstruction methods.

Фото профиля Ruiqi Gao

Ruiqi Gao2 лет назад

CAT3D can create entire 3D scenes in as little as one minute! ⚡ Plus, it achieves state-of-the-art performance for few-view 3D reconstruction.

Фото профиля Ruiqi Gao

Ruiqi Gao2 лет назад

Big shout out to the incredible team: @holynski_ @philipphenzler @ArthurBrussee @rmbrualla @_pratul_ @jon_barron @poolio

Фото профиля Ruiqi Gao

Ruiqi Gao2 лет назад

We are also sincerely thankful to @shlomifruchter, @sirbayes, @babaeizadeh, @Han_Zhang_ and Amir Hertz for training the base text-to-image model that supports our work!

Фото профиля Durk Kingma

Durk Kingma2 лет назад

@holynski_ @poolio Congrats, great work!

Фото профиля Ruiqi Gao

Ruiqi Gao2 лет назад

@holynski_ @poolio Thanks Durk! ☺️

Фото профиля Abubakar Abid

Abubakar Abid2 лет назад

@holynski_ @poolio So cool! @Gradio demo when ;)

Фото профиля Rundi Wu

Rundi Wu2 лет назад

@holynski_ @poolio Amazing results!!! Congrats!

Фото профиля Dejia Xu

Dejia Xu2 лет назад

@holynski_ @poolio Congrats! Amazing results!

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We’ve upgraded Stable Video Diffusion 4D to Stable Video 4D 2.0 (SV4D 2.0), improving the quality of 4D outputs generated from a single object-centric video. While 3D provides a static view of an object’s shape and size; 4D extends this by including time, showing how the object moves. This multi-view video diffusion model generates a 4D output in three steps: 1️⃣ Starts with an input video of a moving person or object 2️⃣ Generates novel views of the subject from unseen angles 3️⃣ Constructs a single dynamic 4D video output with spatial and temporal consistency You can learn more here: (1/4)

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