What if we want to build a nice topology around a pointcloud similar to mesh retopology? Let’s think about it.
Retopologize the 3D data into a quad-like surface mesh is critical for 3D asset generation. Personally, I like the recent paper Reliable Feature-Line Driven Quad-Remeshing and aim to explain it.
Given a triangle mesh $\mathcal{M}$, it aims to partition the mesh into surface patches satisfying several metrics, including
我们的目标是成立黄埔雅苑业主委员会，合理合法的有效伸张并行使业主的权力。为了达到这个目标，第一步是深刻理解深圳市业主委员会成立的筹办流程和细节，并识别其中的风险和挑战。本文旨在对政府文件《成立业主委员会的法定流程》进行拆解分析。
另外补充最权威的官方材料：
Image prior supervision is a topic under the 3D texturing task. Although as we experimented in SDS, Highres-optim and ControlNet, optimizing the color guided by SDS with ControlNet guidance is sufficient to generate good texture, efficiency and editing is hard to address. Therefore, I believe it is an important to study about editing images and using them to guide the texturing.
There are two different problems to study to achieve the goal.
We aim to generate geometry with a high-resolution appearance. The only way is to zoom in since stable diffusion has limited resolution.
There are several options for zoom-in. The first question is whether to use a multi-resolution geometry model, and the second question is the way to zoom in for renderings.
We will investigate this problem in the experiments of 3D representations.
I conclude several ways to zoom in.
We introduce additional geometry controls to better align the appearance with geometry. As from introduction, geometry alignment can be achieved by incorporating geometry features for score distillation (align geometry with the rendering), or controlling the RGB to align with geometry features.
Control Conditions. One of the most popular choices for aligning color to geometry is via controlnet. For example, we can render geometry features to views as edge/normal/depth. Avatarverse further controls character appearance with dense-pose features.
From my experiments, different geometry features contribute to the control in various ways.
According to my introduction, the simplest experiment to start with is the single-view text-to-image experiment using the stochastic optimization method. The reason that we want to optimize rather than denoise is that we extend to 3D, we need to seamlessly generate textures from multiple views and stochastic optimization appears to be the most promising method. In this article, I am going to investigate all the details related to the optimization process.
Given a view parameterized by $\theta$, we can render it via $\mathcal{R}(\theta)$. For example, $\theta$ can represent a vector of pixel RGB/Latent, and $\mathcal{R}$ as the identical mapping. A more advanced version parameterizes $\mathcal{R}$ as a hash encoding followed by a tiny MLP according to InstantNGP. As from DreamFusion, the SDS loss can be represented as:
With the recent progress in the field of generative AI, the community observes new opportunities for 3D content generation exploiting novel neural network architectures pre-trained on large-scale 2D/3D datasets. This article aims to derive the picture of major components that hundreds of recently published papers work on and contribute to this field.
Input and output X-to-3D, in a nutshell, aims to convert some input information to 3D content. To expand it, input is mainly considered as text and image, while an image can be a single-view or multi-view of the same physical object. For 3D, the content can be a tiny object, or an indoor/outdoor scene, each of which faces different challenges. While we mainly discuss the object level since it has great value to the industry and could be the easiest one to handle, some approaches can be extended to work on scenes for potential AR/VR applications.
The input is easy to understand. The input is text/image which can be encoded as latent features. Images can be directly used as supervision for renderings considering the RGB and depth. Note that the image could also be potentially associated with a camera pose.
While the input is quite clear, 3D content can be represented in different forms:
洞见（Why buddhism is true）是美国的一位学者站在现代科学视角下看待佛学的一本书。其目的是抛去其权威、神秘主义、过时的思想，剖析并介绍佛学中较为公认的，与现代科学和社会相符合的观点。书中在达尔文自然选择学说的前提下诠释佛学的合理性，并展现了几个有趣的心理学实验用以佐证佛家的论点，并结合自身体验讲述了正念冥想的价值。其论述可读性强，论据翔实，并结合了作者自身的一些思辨，去其糟粕取其精华。本文意在阐明并解释其主要观点，讨论它与西方哲学的联系和差异，并引入自己的批判和思考。
本书阐释的佛学，其观点我总结如下：
1 | 人受“五蕴”之苦，生而不自由。需进行“八正道”修练（书中主讲正定+正念），逐渐进入“无我”、“无相”的状态，从而得到解脱。 |
展开来讲，主要有几个关键的概念。