Podcasts about 3d mesh

In this work, we introduce Unique3D, a novel image-to-3D framework for efficiently generating high-quality 3D meshes from single-view images, featuring state-of-the-art generation fidelity and strong generalizability. Previous methods based on Score Distillation Sampling (SDS) can produce diversified 3D results by distilling 3D knowledge from large 2D diffusion models, but they usually suffer from long per-case optimization time with inconsistent issues. Recent works address the problem and generate better 3D results either by finetuning a multi-view diffusion model or training a fast feed-forward model. However, they still lack intricate textures and complex geometries due to inconsistency and limited generated resolution. To simultaneously achieve high fidelity, consistency, and efficiency in single image-to-3D, we propose a novel framework Unique3D that includes a multi-view diffusion model with a corresponding normal diffusion model to generate multi-view images with their normal maps, a multi-level upscale process to progressively improve the resolution of generated orthographic multi-views, as well as an instant and consistent mesh reconstruction algorithm called ISOMER, which fully integrates the color and geometric priors into mesh results. Extensive experiments demonstrate that our Unique3D significantly outperforms other image-to-3D baselines in terms of geometric and textural details. 2024: Kailu Wu, Fangfu Liu, Zhihan Cai, Runjie Yan, Hanyang Wang, Yating Hu, Yueqi Duan, Kaisheng Ma https://arxiv.org/pdf/2405.20343

We present InstantMesh, a feed-forward framework for instant 3D mesh generation from a single image, featuring state-of-the-art generation quality and significant training scalability. By synergizing the strengths of an off-the-shelf multiview diffusion model and a sparse-view reconstruction model based on the LRM architecture, InstantMesh is able to create diverse 3D assets within 10 seconds. To enhance the training efficiency and exploit more geometric supervisions, e.g, depths and normals, we integrate a differentiable iso-surface extraction module into our framework and directly optimize on the mesh representation. Experimental results on public datasets demonstrate that InstantMesh significantly outperforms other latest image-to-3D baselines, both qualitatively and quantitatively. We release all the code, weights, and demo of InstantMesh, with the intention that it can make substantial contributions to the community of 3D generative AI and empower both researchers and content creators. 2024: Jiale Xu, Weihao Cheng, Yiming Gao, Xintao Wang, Shenghua Gao, Ying Shan https://arxiv.org/pdf/2404.07191v2.pdf

Today on The AI Breakdown, a research recap of the most interesting recent AI research, including: DreamDiffusion translating EEG to images - https://huggingface.co/papers/2306.16934 Nemo simulating life in games - https://www.ranmo.me/blog/title-digital-companionship Single Image to 3D Mesh in 45 Seconds - https://huggingface.co/papers/2306.16928 CSM any image to 3D - https://www.csm.ai/any-image-to-3d CSM Discord - https://discord.com/invite/NhJJwmk8gT Playground mixed image editing - https://playgroundai.com/ The AI Breakdown helps you understand the most important news and discussions in AI.    Subscribe to The AI Breakdown newsletter: https://theaibreakdown.beehiiv.com/subscribe   Subscribe to The AI Breakdown on YouTube: https://www.youtube.com/@TheAIBreakdown   Join the community: bit.ly/aibreakdown   Learn more: http://breakdown.network/

The pair you are about to hear are not professionals. Their opinions and beliefs are not fact. They are just two idiots that are Spitting Nonsense. Hi, We are Jasmine and Zach here to present you with some nerdy news! We upload our news podcast on Wednesdays and our bonus episode on Saturdays! Support us by following us on Discord at: discord.gg/yjxsKww Give us feedback and let us know how you feel in our #questions-and-suggestions channel on the Discord listed above. --- Send in a voice message: https://anchor.fm/spittingnonsense/message

Curve fitting by mesh and spline --- Send in a voice message: https://anchor.fm/david-nishimoto/message

Camille Scherrer, Ecole Polytechnique Fédérale de Lausanne

Camille Scherrer, Ecole Polytechnique Fédérale de Lausanne

Camille Scherrer, Ecole Polytechnique Fédérale de Lausanne

Camille Scherrer, Ecole Polytechnique Fédérale de Lausanne

Camille Scherrer, Ecole Polytechnique Fédérale de Lausanne