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🚀 Introducing #DreamCraft3D, our breakthrough hierarchical technique for 3D content generation. Leading the way in AIGC, we're redefining 3D generation: Project page: Paper: Code:

16,400 Aufrufe • vor 2 Jahren •via X (Twitter)

10 Kommentare

Profilbild von muhammed rekani
muhammed rekanivor 1 Jahr

Who is here after the R1 storm 😂

Profilbild von Adams Pro 𝕏 👨‍💻👓
Adams Pro 𝕏 👨‍💻👓vor 1 Jahr

Have to check your first tweet

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DeepSeek Community ®vor 1 Jahr

This was the first post (tweet) I found from the DeepSeek profile, and no one could have imagined at the time that this company would take off like a rocket 🚀 and surpass the world's leading AI models. Great job, team! 👏

Profilbild von matt
mattvor 1 Jahr

@FlippingProfits they really fw u huh

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RIckkvor 1 Jahr

DreamCraft3D FTWpVA3wPBBCD8Vdr8Vshfhow7BvVE7sPmqFKu7Wpump

Profilbild von Sophisticated_Brat
Sophisticated_Bratvor 1 Jahr

Damnnnn

Profilbild von ram4nd
ram4ndvor 1 Jahr

This is crazy

Profilbild von Catherine Li
Catherine Livor 1 Jahr

光速 VS. 宇宙膨胀的速度<1

Profilbild von Mozzie.
Mozzie.vor 1 Jahr

Feel like being the whales stomach

Profilbild von ShadowFates 🐦
ShadowFates 🐦vor 1 Jahr

where we go, where this legendary A.I gets Started

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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.

AK

161,530 Aufrufe • vor 2 Jahren