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#DiveIntoDreams 3D Render Challenge Result Announcement & TOP 30 Montage revealed! Layers of dreams wrapped in the song of a choir, this is the festival that the 3D community worked hard to create. Come experience this wonder and splendor... ▼ Outstanding Entry List Huge thanks to Clinton Jones and...

430,686 görüntüleme • 2 yıl önce •via X (Twitter)

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Emanator of Kiana Kaslana profil fotoğrafı
Emanator of Kiana Kaslana2 yıl önce

Kiana and Acheron!!

Ryutatzu profil fotoğrafı
Ryutatzu2 yıl önce

ACHERON WITH KIANA MY BABIEESSS AND THE TV PICTURES OF THE HUGGG 😭😭😭

⊱♧⊰ profil fotoğrafı
⊱♧⊰2 yıl önce

Sparkle leave my man alone hes had enough 😭

Luma profil fotoğrafı
Luma2 yıl önce

They're so precious 🥹

Kary💜 profil fotoğrafı
Kary💜2 yıl önce

Kiana and Acheron Omg yesss 🛐

local Inamei (🐙(🔍)🪶) enjoyer profil fotoğrafı
local Inamei (🐙(🔍)🪶) enjoyer2 yıl önce

Man ... ... ...

Leng profil fotoğrafı
Leng2 yıl önce

my entry didn't make it to the top 30 in the 3D Honkai Challenge. However, I'm eager to learn from this experience. If there is any feedback or insights to share, I'd appreciate it.

✦ profil fotoğrafı
2 yıl önce

Dear developers, I have a question, why wasn't this in top 30? Was this work disqualified?

XldoJxir profil fotoğrafı
XldoJxir2 yıl önce

Sale Archeron con su Kiana no mms

Benzer Videolar

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 görüntüleme • 2 yıl önce