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📢WorldAgents: 3D worlds only from 2D image models - without any training! We propose an agentic approach with a Director (VLM) to plan the scene, a Generator (Flux or NanoBanana) for new views, and a Verifier (VLM) for selection / 3D consistency. -> High-fidelity 3D worlds from a single... show more

Matthias Niessner

49,570 subscribers

19,093 просмотров • 7 месяцев назад •via X (Twitter)

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Комментарии: 4

Фото профиля Katja Schwarz

Katja Schwarz7 месяцев назад

@ErkocZiya Our work [2503.16611] A Recipe for Generating 3D Worlds From a Single Image was among the first to show how to use image models like Flux to generate consistent worlds. You are missing the citation in the related work and attribution of credit.

Фото профиля LionTV

LionTV7 месяцев назад

agents cooking 3D from 2D this is insane alpha drop

Фото профиля Elon Orbit

Elon Orbit7 месяцев назад

Director-Generator-Verifier pipeline. Solid. But anyone seen the compute bill? That's where these papers get quiet.

Фото профиля Jennifer Brauner

Jennifer Brauner7 месяцев назад

nice

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📢 A Recipe for Generating 3D Worlds From a Single Image 📢 Our recipe explains how existing generative models can be adapted with minimal training effort to generate 3D worlds from a single input image.

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Katja Schwarz

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Marble is the first product from World Labs and is powered by our multimodal world model, which lets anyone create high-fidelity, persistent 3D worlds from just a single image, video, text prompt, or 3D layout. Read more at

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Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors paper page: present Magic123, a two-stage coarse-to-fine approach for high-quality, textured 3D meshes generation from a single unposed image in the wild using both2D and 3D priors. In the first stage, we optimize a neural radiance field to produce a coarse geometry. In the second stage, we adopt a memory-efficient differentiable mesh representation to yield a high-resolution mesh with a visually appealing texture. In both stages, the 3D content is learned through reference view supervision and novel views guided by a combination of 2D and 3D diffusion priors. We introduce a single trade-off parameter between the 2D and 3D priors to control exploration (more imaginative) and exploitation (more precise) of the generated geometry. Additionally, we employ textual inversion and monocular depth regularization to encourage consistent appearances across views and to prevent degenerate solutions, respectively. Magic123 demonstrates a significant improvement over previous image-to-3D techniques, as validated through extensive experiments on synthetic benchmarks and diverse real-world images.

Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors paper page: present Magic123, a two-stage coarse-to-fine approach for high-quality, textured 3D meshes generation from a single unposed image in the wild using both2D and 3D priors. In the first stage, we optimize a neural radiance field to produce a coarse geometry. In the second stage, we adopt a memory-efficient differentiable mesh representation to yield a high-resolution mesh with a visually appealing texture. In both stages, the 3D content is learned through reference view supervision and novel views guided by a combination of 2D and 3D diffusion priors. We introduce a single trade-off parameter between the 2D and 3D priors to control exploration (more imaginative) and exploitation (more precise) of the generated geometry. Additionally, we employ textual inversion and monocular depth regularization to encourage consistent appearances across views and to prevent degenerate solutions, respectively. Magic123 demonstrates a significant improvement over previous image-to-3D techniques, as validated through extensive experiments on synthetic benchmarks and diverse real-world images.

AK

305,749 просмотров • 3 лет назад

Text2Room: Extracting Textured 3D Meshes from 2D Text-to-Image Models abs: project page:

Text2Room: Extracting Textured 3D Meshes from 2D Text-to-Image Models abs: project page:

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277,316 просмотров • 3 лет назад

(1/4) Excited to share our #ICCV2023 paper Text2Room! We generate scene-scale textured 3D meshes from a given text prompt leveraging 2D text-to-image models such as StableDiffusion. Project: Code: Video:

(1/4) Excited to share our #ICCV2023 paper Text2Room! We generate scene-scale textured 3D meshes from a given text prompt leveraging 2D text-to-image models such as StableDiffusion. Project: Code: Video:

Matthias Niessner

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🔥 New Feature: Multi-View Images in One Click! 🎨 Turn text/image → consistent multi-views (powered by Flux Kontext) → high-accuracy 3D models in seconds! 🚀 Boost your 3D workflow today:

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MeshyAI

10,461 просмотров • 1 год назад

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One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimization paper page: Single image 3D reconstruction is an important but challenging task that requires extensive knowledge of our natural world. Many existing methods solve this problem by optimizing a neural radiance field under the guidance of 2D diffusion models but suffer from lengthy optimization time, 3D inconsistency results, and poor geometry. In this work, we propose a novel method that takes a single image of any object as input and generates a full 360-degree 3D textured mesh in a single feed-forward pass. Given a single image, we first use a view-conditioned 2D diffusion model, Zero123, to generate multi-view images for the input view, and then aim to lift them up to 3D space. Since traditional reconstruction methods struggle with inconsistent multi-view predictions, we build our 3D reconstruction module upon an SDF-based generalizable neural surface reconstruction method and propose several critical training strategies to enable the reconstruction of 360-degree meshes. Without costly optimizations, our method reconstructs 3D shapes in significantly less time than existing methods. Moreover, our method favors better geometry, generates more 3D consistent results, and adheres more closely to the input image. We evaluate our approach on both synthetic data and in-the-wild images and demonstrate its superiority in terms of both mesh quality and runtime. In addition, our approach can seamlessly support the text-to-3D task by integrating with off-the-shelf text-to-image diffusion models.

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179,061 просмотров • 3 лет назад

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15,403 просмотров • 2 месяцев назад

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18,903 просмотров • 1 год назад

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Dreaming Tulpa 🥓👑

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Google DeepMind

1,443,434 просмотров • 1 год назад

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AK

41,747 просмотров • 2 лет назад

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42,914 просмотров • 4 месяцев назад

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marcus

19,367 просмотров • 1 месяц назад

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