In this paper, we introduce X-Ray, an innovative approach to 3D generation
that employs a new sequential representation, drawing inspiration from the
depth-revealing capabilities of X-Ray scans to meticulously capture both the
external and internal features of objects. Central to our method is the
utilization of ray casting techniques originating from the camera's viewpoint,
meticulously recording the geometric and textural details encountered across
all intersected surfaces. This process efficiently condenses complete objects
or scenes into a multi-frame format, just like videos. Such a structure ensures
the 3D representation is composed solely of critical surface information.
Highlighting the practicality and adaptability of our X-Ray representation, we
showcase its utility in synthesizing 3D objects, employing a network
architecture akin to that used in video diffusion models. The outcomes reveal
our representation's superior performance in enhancing both the accuracy and
efficiency of 3D synthesis, heralding new directions for ongoing research and
practical implementations in the field.

通过借鉴 X 射线扫描的深度揭示能力，我们引入了一种创新的 3D 生成方法 X-Ray，它采用了一种新的序列表示方法，精确捕捉了物体的外部和内部特征。我们的方法的核心是利用源自相机视图的光线投射技术，详细记录了所有相交表面上遇到的几何和纹理细节。这个过程可以将完整的物体或场景高效地压缩成一个多帧格式，就像视频一样。这种结构确保了 3D 表示仅由关键表面信息组成。通过展示 X-Ray 表示法在合成 3D 物体方面的实用性和适应性，我们展示了类似于视频扩散模型中使用的网络架构的效用。结果表明，我们的表示法在提高 3D 合成的准确性和效率方面表现出优异性能，为该领域的持续研究和实际应用开辟了新的方向。

X 射线：一种用于生成的序列 3D 表示

X-Ray: A Sequential 3D Representation for Generation

Benefiting from the rapid development of 2D diffusion models, 3D content
creation has made significant progress recently. One promising solution
involves the fine-tuning of pre-trained 2D diffusion models to harness their
capacity for producing multi-view images, which are then lifted into accurate
3D models via methods like fast-NeRFs or large reconstruction models. However,
as inconsistency still exists and limited generated resolution, the generation
results of such methods still lack intricate textures and complex geometries.
To solve this problem, we propose Magic-Boost, a multi-view conditioned
diffusion model that significantly refines coarse generative results through a
brief period of SDS optimization ($\sim15$min). Compared to the previous text
or single image based diffusion models, Magic-Boost exhibits a robust
capability to generate images with high consistency from pseudo synthesized
multi-view images. It provides precise SDS guidance that well aligns with the
identity of the input images, enriching the local detail in both geometry and
texture of the initial generative results. Extensive experiments show
Magic-Boost greatly enhances the coarse inputs and generates high-quality 3D
assets with rich geometric and textural details. (Project Page:
this https URL)

通过 Magic-Boost，使用多视角条件扩散模型可以通过短时间的 SDS 优化显著优化粗糙的生成结果，生成具有丰富几何和纹理细节的高质量 3D 模型。