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📢Pixel3DMM: Versatile Screen-Space Priors for Single-Image 3D Face Reconstruction📢 -> highly accurate face reconstruction by training powerful VITs via surface normals and UV-coordinates estimation. The geometric cues from our 2D foundation model backbone constrain the 3DMM parameters, which allows us to achieve remarkable reconstruction accuracy - works for both... show more

Matthias Niessner

47,486 subscribers

62,013 görüntüleme • 1 yıl önce •via X (Twitter)

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Felix Taubner profil fotoğrafı

Felix Taubner1 yıl önce

It’s face tracker christmas! Great work @SGiebenhain, looking forward to the code :)

HUDI profil fotoğrafı

HUDI1 yıl önce

🚀 Just released: our groundbreaking documentation update for HUDI! 🐸 Dive deep into the innovative DataMask features and explore the future of decentralized data with our new Data Apps, including the revolutionary Health app. Secure, private, and now truly usable—welcome to the next level of Web3 data management! 🌐🔐 👉🔗 #Web3 #DataPrivacy #DataApps #HUDI #DeFi

Bowser profil fotoğrafı

Bowser1 yıl önce

I notice none of your examples have a full beard.

Jscott profil fotoğrafı

Jscott1 yıl önce

Damn thats impressive

df profil fotoğrafı

df1 yıl önce

Is this real-time? How many FPS on what hardware?

rak profil fotoğrafı

rak1 yıl önce

COOLCOOL

rak profil fotoğrafı

rak1 yıl önce

Will it be open source ？

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Matthias Niessner

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📢 Lyra: Generative 3D Scene Reconstruction via Video Diffusion Model Self-Distillation Got only one or a few images and wondering if recovering the 3D environment is a reconstruction or generation problem? Why not do it with a generative reconstruction model! We show that a camera-conditioned video diffusion model can be transformed into a generative reconstruction model that directly outputs a high-quality 3D Gaussian Splatting representation through self-distillation, without requiring real-world training data. Check out our results in the video (wait for dynamic scenes in the second half!) : Project Page: Code and Models: Paper:

Sherwin Bahmani

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LongSplat: Robust Unposed 3D Gaussian Splatting for Casual Long Videos Contributions: • An incremental joint optimization approach for simultaneous camera pose and 3DGS reconstruction, reducing local minima and ensuring global consistency. • A robust pose estimation module leveraging learned 3D priors for accurate camera pose estimation. • An adaptive Octree Anchor Formation strategy that significantly reduces memory usage while preserving reconstruction quality.

MrNeRF

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🚀Turn Single Image into 3D Human🚀 #GeneMAN is a generalizable single-image 3D human reconstruction framework that turns in-the-wild images into high-quality 3D humans with ease 🔗Project: 📜Paper: 🧑‍💻Code:

🚀Turn Single Image into 3D Human🚀 #GeneMAN is a generalizable single-image 3D human reconstruction framework that turns in-the-wild images into high-quality 3D humans with ease 🔗Project: 📜Paper: 🧑‍💻Code:

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Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors paper page: present Magic123, a two-stage coarse-to-fine approach for high-quality, textured 3D meshes generation from a single unposed image in the wild using both2D and 3D priors. In the first stage, we optimize a neural radiance field to produce a coarse geometry. In the second stage, we adopt a memory-efficient differentiable mesh representation to yield a high-resolution mesh with a visually appealing texture. In both stages, the 3D content is learned through reference view supervision and novel views guided by a combination of 2D and 3D diffusion priors. We introduce a single trade-off parameter between the 2D and 3D priors to control exploration (more imaginative) and exploitation (more precise) of the generated geometry. Additionally, we employ textual inversion and monocular depth regularization to encourage consistent appearances across views and to prevent degenerate solutions, respectively. Magic123 demonstrates a significant improvement over previous image-to-3D techniques, as validated through extensive experiments on synthetic benchmarks and diverse real-world images.

AK

305,643 görüntüleme • 2 yıl önce

Early R&D using our SP-6M dataset. Exploring image-to-3D reconstruction from single images, including heavily modified inputs (lighting, hair, etc). Still a work in progress.

Early R&D using our SP-6M dataset. Exploring image-to-3D reconstruction from single images, including heavily modified inputs (lighting, hair, etc). Still a work in progress.

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