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Comfy3D Update: (v0.1.5.alpha, dev branch) - Integrated TripoSG (Plus scribble model) - MV-Adapter for high quality texture gen coming next ‼️NeuralAI We are hiring skilled ML researchers & engineers (VAE, diffusion model, 3D/texture gen/editing) to join us on a mission to revolutionize 3D virtual production 🫰If you want to...

14,716 просмотров • 1 год назад •via X (Twitter)

Комментарии: 8

Фото профиля Mr. For Example
Mr. For Example1 год назад

TripoSG Scribble Model also works

Фото профиля ksminnovation
ksminnovation1 год назад

AI is transforming healthcare! A KSM-led study shows AI can detect Celiac disease 4 years earlier @TalPatalon @MedPredict

Фото профиля Tripo
Tripo1 год назад

@GoNeuralAI thanks for what you have built for the community!

Фото профиля Mr. For Example
Mr. For Example1 год назад

@GoNeuralAI My pleasure🍻

Фото профиля pite-chen
pite-chen1 год назад

@GoNeuralAI nice

Фото профиля 0xNano
0xNano1 год назад

@GoNeuralAI 🔥🔥🔥

Фото профиля David
David1 год назад

@GoNeuralAI $NEURAL 🔥

Фото профиля Skiny3604
Skiny36041 год назад

@GoNeuralAI Future of #ai #gaming $NEURAL 🔥🚀

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DreamCraft3D: Hierarchical 3D Generation with Bootstrapped Diffusion Prior paper page: present DreamCraft3D, a hierarchical 3D content generation method that produces high-fidelity and coherent 3D objects. We tackle the problem by leveraging a 2D reference image to guide the stages of geometry sculpting and texture boosting. A central focus of this work is to address the consistency issue that existing works encounter. To sculpt geometries that render coherently, we perform score distillation sampling via a view-dependent diffusion model. This 3D prior, alongside several training strategies, prioritizes the geometry consistency but compromises the texture fidelity. We further propose Bootstrapped Score Distillation to specifically boost the texture. We train a personalized diffusion model, Dreambooth, on the augmented renderings of the scene, imbuing it with 3D knowledge of the scene being optimized. The score distillation from this 3D-aware diffusion prior provides view-consistent guidance for the scene. Notably, through an alternating optimization of the diffusion prior and 3D scene representation, we achieve mutually reinforcing improvements: the optimized 3D scene aids in training the scene-specific diffusion model, which offers increasingly view-consistent guidance for 3D optimization. The optimization is thus bootstrapped and leads to substantial texture boosting. With tailored 3D priors throughout the hierarchical generation, DreamCraft3D generates coherent 3D objects with photorealistic renderings, advancing the state-of-the-art in 3D content generation.

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161,400 просмотров • 2 лет назад