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All of these 3D objects were generated entirely via AI (each from a single image) and the results are reaching exceptional levels of detail and fidelity. • Voxel Resolution: 1536^3 • Texture Resolution: 4096*4096 Pretty incredible stuff.

159,060 просмотров • 1 год назад •via X (Twitter)

Комментарии: 10

Фото профиля Emm | scenario.com
Emm | scenario.com1 год назад

I used Sparc3D ( with the new texturing option. It works beautifully on both standalone objects (characters, props etc.) or full scenes like this one.

Фото профиля Emm | scenario.com
Emm | scenario.com1 год назад

I MEAN CHECK THIS. 🥹🤯 (The reference Image was generated on @Scenario_gg using a "Isometric Skeuomorphic 3D" LoRA, inspired by the new AirBnB icons)

Фото профиля Emm | scenario.com
Emm | scenario.com1 год назад

Another one 👇 Left: Generated 3D Model (Blender) Right: Reference Image

Фото профиля Emm | scenario.com
Emm | scenario.com1 год назад

On the realistic/semi-realistic side "Pale dinosaur-like mount with sharp teeth and powerful limbs" (reference image initially generated on Scenario with Flux 1.1 Pro Ultra)

Фото профиля Matthew Nagy
Matthew Nagy1 год назад

AI art is leveling up holy🔥🔥

Фото профиля TomLikesRobots🤖
TomLikesRobots🤖1 год назад

These look great. This tech is really maturing.

Фото профиля Søren Bramer Schmidt
Søren Bramer Schmidt1 год назад

Could these be sent directly to a 3d printer?

Фото профиля Emm | scenario.com
Emm | scenario.com1 год назад

Convert to STL, slice in your 3D printing software, and print ✅

Фото профиля Sebastian BO
Sebastian BO1 год назад

Absolutely incredible, didn’t know there was a texture option with Sparc3D 🥳

Фото профиля BattleRise: Kingdom of Champions
BattleRise: Kingdom of Champions1 год назад

Would ya look at those models 👀🙌🏼

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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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