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Introducing 𝐆𝐚𝐮𝐬𝐬𝐢𝐚𝐧 𝐀𝐧𝐲𝐭𝐡𝐢𝐧𝐠, a new 3D generative model with two key properties: - A structured point-cloud latent space enabling flexible editing! - Support multi-modal conditions, e.g., point cloud, text, single/multi-view images arXiv:

Xingang Pan

3,057 subscribers

55,847 görüntüleme • 1 yıl önce •via X (Twitter)

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Xingang Pan profil fotoğrafı

Xingang Pan1 yıl önce

Project page:

Zoe Wang profil fotoğrafı

Zoe Wang1 yıl önce

The paper introduces a comprehensive 3D generation framework with a point cloud-structured 3D latent space, addressing the limitations of existing methods.

HELLCAT profil fotoğrafı

HELLCAT1 yıl önce

gaussian anything? more like reality collapse. can't wait to break this one too.

Angel profil fotoğrafı

Angel1 yıl önce

The ability to edit in a structured point-cloud space is a huge leap forward.

AnonymousDestiny profil fotoğrafı

AnonymousDestiny1 yıl önce

I'm super glad there's finally a 3D generative model that allows multiple images to improve the geometry of the output!

Ostap Hembara profil fotoğrafı

Ostap Hembara1 yıl önce

This animation has bought me right away, I`ll have to read this now.

Artur Formella profil fotoğrafı

Artur Formella1 yıl önce

What if I have 8 images of the object?

Synthical profil fotoğrafı

Synthical1 yıl önce

Dark mode for this paper 🌙

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Tencent Hy

46,892 görüntüleme • 10 ay önce

📢📢 𝐏𝐞𝐫𝐜𝐇𝐞𝐚𝐝: 𝐏𝐞𝐫𝐜𝐞𝐩𝐭𝐮𝐚𝐥 𝐇𝐞𝐚𝐝 𝐌𝐨𝐝𝐞𝐥 𝐟𝐨𝐫 𝐒𝐢𝐧𝐠𝐥𝐞-𝐈𝐦𝐚𝐠𝐞 𝟑𝐃 𝐇𝐞𝐚𝐝 𝐑𝐞𝐜𝐨𝐧𝐬𝐭𝐫𝐮𝐜𝐭𝐢𝐨𝐧 & 𝐄𝐝𝐢𝐭𝐢𝐧𝐠📢📢 PercHead reconstructs realistic 3D heads from a single image and enables disentangled 3D editing via geometric controls and style inputs from images or text. At its core is a generalized 3D head decoder trained with perceptual supervision from DINOv2 and SAM 2.1. We find that our new perceptual loss formulation improves reconstruction fidelity compared to commonly-used methods such as LPIPS. Our trained reconstruction model is able to generate 3D-consistent heads from a single input image. Even with challenging side-view inputs, the model robustly infers missing regions for a coherent, high-fidelity output. In addition, our architecture seamlessly adapts to downstream tasks: by swapping the encoder, we can transform the model into a disentangled 3D editing pipeline. In this scenario, we can control geometry through - potentially hand-drawn - segmentation maps, and condition style via image or text prompt. We also provide an interactive GUI to enable the exploration of our editing pipeline. 🌍 📽️ Great work by Antonio Oroz and Tobias Kirschstein

📢📢 𝐏𝐞𝐫𝐜𝐇𝐞𝐚𝐝: 𝐏𝐞𝐫𝐜𝐞𝐩𝐭𝐮𝐚𝐥 𝐇𝐞𝐚𝐝 𝐌𝐨𝐝𝐞𝐥 𝐟𝐨𝐫 𝐒𝐢𝐧𝐠𝐥𝐞-𝐈𝐦𝐚𝐠𝐞 𝟑𝐃 𝐇𝐞𝐚𝐝 𝐑𝐞𝐜𝐨𝐧𝐬𝐭𝐫𝐮𝐜𝐭𝐢𝐨𝐧 & 𝐄𝐝𝐢𝐭𝐢𝐧𝐠📢📢 PercHead reconstructs realistic 3D heads from a single image and enables disentangled 3D editing via geometric controls and style inputs from images or text. At its core is a generalized 3D head decoder trained with perceptual supervision from DINOv2 and SAM 2.1. We find that our new perceptual loss formulation improves reconstruction fidelity compared to commonly-used methods such as LPIPS. Our trained reconstruction model is able to generate 3D-consistent heads from a single input image. Even with challenging side-view inputs, the model robustly infers missing regions for a coherent, high-fidelity output. In addition, our architecture seamlessly adapts to downstream tasks: by swapping the encoder, we can transform the model into a disentangled 3D editing pipeline. In this scenario, we can control geometry through - potentially hand-drawn - segmentation maps, and condition style via image or text prompt. We also provide an interactive GUI to enable the exploration of our editing pipeline. 🌍 📽️ Great work by Antonio Oroz and Tobias Kirschstein

Matthias Niessner

18,855 görüntüleme • 8 ay önce

We are thrilled to release the next leap in art-grade 3D generative models. Our single-click model pipeline gives unprecedented mesh outputs, with mesh parts-based topology. It is available now for all Cube tiers to start for free. ☑️ Our multi-stage hierarchical AI models produce a fully assembled 3D mesh with adaptive poly-counts, providing the clean, separated topology you need. ✅ A parts-based approach enables high-resolution meshes. 🌐 Quad and Triangle mesh support. Access now:

We are thrilled to release the next leap in art-grade 3D generative models. Our single-click model pipeline gives unprecedented mesh outputs, with mesh parts-based topology. It is available now for all Cube tiers to start for free. ☑️ Our multi-stage hierarchical AI models produce a fully assembled 3D mesh with adaptive poly-counts, providing the clean, separated topology you need. ✅ A parts-based approach enables high-resolution meshes. 🌐 Quad and Triangle mesh support. Access now:

Common Sense Machines

420,441 görüntüleme • 1 yıl önce