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.

我们介绍了 Unique3D，这是一种新颖的图像到 3D 框架，能够高效生成高质量的 3D 网格，具有最先进的生成质量和强大的泛化能力。我们的 Unique3D 在几何和纹理细节方面显著优于其他图像到 3D 基准。

Unique3D：基于单张图片的高质量高效 3D 网格生成

Unique3D: High-Quality and Efficient 3D Mesh Generation from a Single  Image

In open domain table-to-text generation, we notice that the unfaithful
generation usually contains hallucinated content which can not be aligned to
any input table record. We thus try to evaluate the generation faithfulness
with two entity-centric metrics: table record coverage and the ratio of
hallucinated entities in text, both of which are shown to have strong agreement
with human judgements. Then based on these metrics, we quantitatively analyze
the correlation between training data quality and generation fidelity which
indicates the potential usage of entity information in faithful generation.
Motivated by these findings, we propose two methods for faithful generation: 1)
augmented training by incorporating the auxiliary entity information, including
both an augmented plan-based model and an unsupervised model and 2) training
instance selection based on faithfulness ranking. We show these approaches
improve generation fidelity in both full dataset setting and few shot learning
settings by both automatic and human evaluations.

本文旨在评估表格生成的忠实度，并采用两个与实体相关的度量标准来评估生成忠实度，并基于这些数据提出了两种增强训练方法和训练实例选择方法来提高生成保真度，在完整数据集和少量数据集学习中均有所改善。