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🎉We present DreamCraft3D++, an extension of DreamCraft3D that enables efficient high-quality generation of complex 3D assets in 10 minutes. Project: Paper:

28,318 görüntüleme • 1 yıl önce •via X (Twitter)

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Neuro profil fotoğrafı
Neuro1 yıl önce

The output looks amazing, although 10 mins is quite some time. Really curious to check out the code release mate, congrats 🔥

Synthical profil fotoğrafı
Synthical1 yıl önce

Dark mode for this paper for night readers 🌚

Bobcat profil fotoğrafı
Bobcat1 yıl önce

Amazing

Luc 💭 🇺🇸 🇺🇦 🇧🇷 💭 profil fotoğrafı
Luc 💭 🇺🇸 🇺🇦 🇧🇷 💭1 yıl önce

What size gpu does this require??

Jingxiang Sun profil fotoğrafı
Jingxiang Sun1 yıl önce

Hi, we use A100-80G gpus for training.

Peng Su profil fotoğrafı
Peng Su1 yıl önce

Nice work, Jinxaing. Are you interest in building L5 self-driving agent at industry-level dataset , i.e.~1 M fleet .

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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

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