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From 3d mesh optimization for games to accurate CAD to 3D conversions for enterprise and workflows that turbo-charge every 3D asset pipeline: InstaLOD is everything you need for the production and automatic optimization of 3D content.

17,283,631 просмотров • 3 лет назад •via X (Twitter)

Комментарии: 10

Фото профиля star wyse
star wyse3 лет назад

hey this is the thing that was used in sonic frontiers

Фото профиля InstaLOD
InstaLOD3 лет назад

Indeed, there's a video on the production on our YouTube channel!

Фото профиля VentiVR - Archived - 🦋
VentiVR - Archived - 🦋2 лет назад

Frontiers Mentioned

Фото профиля IZZY ♻️🥷®️
IZZY ♻️🥷®️2 лет назад

Nice technology

Фото профиля Joe Dickenson | Co-Founder Candyland Carnage
Joe Dickenson | Co-Founder Candyland Carnage2 лет назад

This kind of thing in 3D world is insanely useful and time saving. Need a lot more stuff like this 🔥

Фото профиля Aezon
Aezon2 лет назад

This thing is super useful

Фото профиля ooOPizzaHeadOoo
ooOPizzaHeadOoo3 лет назад

does it keep quads?

Фото профиля 🐲❄️🔥ShiningMew⚡️❄️🐲
🐲❄️🔥ShiningMew⚡️❄️🐲2 лет назад

hey i know this! it’s the tech used to make the lod models for sonic frontiers! really nice work!

Фото профиля Bluwolfblitz
Bluwolfblitz2 лет назад

Is there a free license for aspiring 3D Artists?

Фото профиля InstaLOD
InstaLOD2 лет назад

There is! You can get the pioneer license for free at - it's fully featured! 🤩

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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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