Many species face the threat of extinction. It's important to study these
species and gather information about them as much as possible to preserve
biodiversity. Due to the rarity of endangered species, there is a limited
amount of data available, making it difficult to apply data requiring
generative AI methods to this domain. We aim to study the feasibility of
generating consistent and real-like 3D models of endangered animals using
limited data. Such a phenomenon leads us to utilize zero-shot stable diffusion
models that can generate a 3D model out of a single image of the target
species. This paper investigates the intricate relationship between elevation
angle and the output quality of 3D model generation, focusing on the innovative
approach presented in DreamGaussian. DreamGaussian, a novel framework utilizing
Generative Gaussian Splatting along with novel mesh extraction and refinement
algorithms, serves as the focal point of our study. We conduct a comprehensive
analysis, analyzing the effect of varying elevation angles on DreamGaussian's
ability to reconstruct 3D scenes accurately. Through an empirical evaluation,
we demonstrate how changes in elevation angle impact the generated images'
spatial coherence, structural integrity, and perceptual realism. We observed
that giving a correct elevation angle with the input image significantly
affects the result of the generated 3D model. We hope this study to be
influential for the usability of AI to preserve endangered animals; while the
penultimate aim is to obtain a model that can output biologically consistent 3D
models via small samples, the qualitative interpretation of an existing
state-of-the-art model such as DreamGaussian will be a step forward in our
goal.

有限的数据条件下，研究了使用生成对抗网络生成濒危物种 3D 模型的可行性，探讨了海拔角度对生成结果的质量的影响，旨在促进人工智能在保护濒危动物方面的应用。

使用 DreamGaussian 探索生成真实的濒危物种 3D 模型的可行性：高度角对模型生成的影响分析

Exploring the Feasibility of Generating Realistic 3D Models of  Endangered Species Using DreamGaussian: An Analysis of Elevation Angle's  Impact on Model Generation

The recent advances in text and image synthesis show a great promise for the
future of generative models in creative fields. However, a less explored area
is the one of 3D model generation, with a lot of potential applications to game
design, video production, and physical product design. In our paper, we present
3DGEN, a model that leverages the recent work on both Neural Radiance Fields
for object reconstruction and GAN-based image generation. We show that the
proposed architecture can generate plausible meshes for objects of the same
category as the training images and compare the resulting meshes with the
state-of-the-art baselines, leading to visible uplifts in generation quality.

我们提出了 3DGEN 模型，它利用最近在物体重建和基于 GAN 的图像生成方面的工作，可以为训练图像的同一类别对象生成可信的三维网格，并将生成的网格与当前最先进的基准模型进行比较，取得了可见的生成质量改进。

3DGEN：一种基于 GAN 的从图像数据生成新的 3D 模型的方法

3DGEN: A GAN-based approach for generating novel 3D models from image  data

The demand for efficient 3D model generation techniques has grown
exponentially, as manual creation of 3D models is time-consuming and requires
specialized expertise. While generative models have shown potential in creating
3D textured shapes from 2D images, their applicability in 3D industries is
limited due to the lack of a well-defined camera distribution in real-world
scenarios, resulting in low-quality shapes. To overcome this limitation, we
propose GET3D--, the first method that directly generates textured 3D shapes
from 2D images with unknown pose and scale. GET3D-- comprises a 3D shape
generator and a learnable camera sampler that captures the 6D external changes
on the camera. In addition, We propose a novel training schedule to stably
optimize both the shape generator and camera sampler in a unified framework. By
controlling external variations using the learnable camera sampler, our method
can generate aligned shapes with clear textures. Extensive experiments
demonstrate the efficacy of GET3D--, which precisely fits the 6D camera pose
distribution and generates high-quality shapes on both synthetic and realistic
unconstrained datasets.

GET3D-- 是第一个通过未知姿态和尺度的 2D 图像直接生成贴图的 3D 形状的方法，它能够精确适应 6D 相机姿态分布，并在合成和真实无约束数据集上生成高质量的形状。

从无约束图像集合中学习 GET3D--

GET3D--: Learning GET3D from Unconstrained Image Collections

The ability to generate highly realistic 2D images from mere text prompts has
recently made huge progress in terms of speed and quality, thanks to the advent
of image diffusion models. Naturally, the question arises if this can be also
achieved in the generation of 3D content from such text prompts. To this end, a
new line of methods recently emerged trying to harness diffusion models,
trained on 2D images, for supervision of 3D model generation using view
dependent prompts. While achieving impressive results, these methods, however,
have two major drawbacks. First, rather than commonly used 3D meshes, they
instead generate neural radiance fields (NeRFs), making them impractical for
most real applications. Second, these approaches tend to produce over-saturated
models, giving the output a cartoonish looking effect. Therefore, in this work
we propose a novel method for generation of highly realistic-looking 3D meshes.
To this end, we extend NeRF to employ an SDF backbone, leading to improved 3D
mesh extraction. In addition, we propose a novel way to finetune the mesh
texture, removing the effect of high saturation and improving the details of
the output 3D mesh.

通过引入 SDF backbon，扩展 NeRF 来进行 3D 模型生成，并通过新颖的方法对网格纹理进行微调以改善输出的细节。