一年前的工作,2D扩散模型加3D NeRF的文本-到-图像合成,“DreamFusion: Text-to-3D Using 2D Diffusion“,来自谷歌和伯克利分校。 以文本为条件的生成图像模型现在支持高保真、多样和可控的图像合成(Nichol22;…
DreamFusion: Text-to-3D Using 2D Diffusion 目前文本对图像的生成技术已经相对比较成熟了(Stablediffusion, dalle2...),这种成功取决于我们可以网上找上数量相当庞大的文本-图像对进行训练。想要实现文本到三维模型生成模型,走条路是不太成了,因为我们并没有这么多的文本-三维模型图像对。那能不能利用现有的文本-...
【DreamFusion: Text-to-3D using 2D Diffusion】https:///dreamfusion3d.github.io/ DreamFusion:使用 2D 扩散的文本到 3D 。 û收藏 5 评论 ñ5 评论 o p 同时转发到我的微博 按热度 按时间 正在加载,请稍候... 互联网科技博主 超话主持人(网路冷眼技术分享超话) 查看更...
We introduce a loss based on probability density distillation that enables the use of a 2D diffusion model as a prior for optimization of a parametric image generator. Using this loss in a DeepDream-like procedure, we optimize a randomly-initialized 3D model (a Neural Radiance Field, or NeRF)...
论文名:DreamFusion: Text-to-3D using 2D Diffusion 发布时间:2022年9月 论文地址:https://arxiv.org/abs/2209.14988 代码地址: 原文摘要:最近在文本-图像合成方面的突破是由在数十亿图像-文本对上训练的扩散模型推动的。将这种方法应用于三维合成需要大规模的标记三维数据集和高效的三维数据去噪架构,而这两者目前...
using a pretrained 2D text-to-image diffusion model to perform text-to-3D synthesis. We introduce a loss based on probability density distillation that enables the use of a 2D diffusion model as a prior for optimization of a parametric image generator. Using this loss in a DeepDream-like ...
References# [MM-MODELS-DF1] Ben Poole, Ajay Jain, Jonathan T. Barron, and Ben Mildenhall. Dreamfusion: text-to-3d using 2d diffusion. 2022. URL:https://arxiv.org/abs/2209.14988.
Text-to-3D & Image-to-3D & Mesh Exportation with NeRF + Diffusion. - ashawkey/stable-dreamfusion
DreamFusion uses a pretrained text-to-image diffusion model to perform text-to-3D synthesis. The model employs a loss based on probability density distillation, enabling the use of a 2D diffusion model as a prior for optimizing a parametric image generator....
DreamFusion: Text-to-3D using 2D Diffusion: https://arxiv.org/abs/2209.14988 Make-A-Video: Text-to-Video Generation without Text-Video Data: https://arxiv.org/abs/2209.14792 Imagen Video: High Definition Video Generation with Diffusion Models: https://arxiv.org/abs/2210.02303 ...