We present a simple, modular, and generic method that upsamples coarse 3D
models by adding geometric and appearance details. While generative 3D models
now exist, they do not yet match the quality of their counterparts in image and
video domains. We demonstrate that it is possible to directly repurpose
existing (pretrained) video models for 3D super-resolution and thus sidestep
the problem of the shortage of large repositories of high-quality 3D training
models. We describe how to repurpose video upsampling models, which are not 3D
consistent, and combine them with 3D consolidation to produce 3D-consistent
results. As output, we produce high quality Gaussian Splat models, which are
object centric and effective. Our method is category agnostic and can be easily
incorporated into existing 3D workflows. We evaluate our proposed SuperGaussian
on a variety of 3D inputs, which are diverse both in terms of complexity and
representation (e.g., Gaussian Splats or NeRFs), and demonstrate that our
simple method significantly improves the fidelity of the final 3D models. Check
our project website for details: supergaussian.github.io

通过重复使用现有的视频模型进行 3D 超分辨率，我们提出了一种简单、模块化和通用的方法，以增加粗糙 3D 模型的几何和外观细节，从而显著提高最终 3D 模型的保真度。

SuperGaussian: 将视频模型用于 3D 超分辨率改造

SuperGaussian: Repurposing Video Models for 3D Super Resolution

We present a method to build animatable dog avatars from monocular videos.
This is challenging as animals display a range of (unpredictable) non-rigid
movements and have a variety of appearance details (e.g., fur, spots, tails).
We develop an approach that links the video frames via a 4D solution that
jointly solves for animal's pose variation, and its appearance (in a canonical
pose). To this end, we significantly improve the quality of template-based
shape fitting by endowing the SMAL parametric model with Continuous Surface
Embeddings, which brings image-to-mesh reprojection constaints that are denser,
and thus stronger, than the previously used sparse semantic keypoint
correspondences. To model appearance, we propose an implicit duplex-mesh
texture that is defined in the canonical pose, but can be deformed using SMAL
pose coefficients and later rendered to enforce a photometric compatibility
with the input video frames. On the challenging CoP3D and APTv2 datasets, we
demonstrate superior results (both in terms of pose estimates and predicted
appearance) to existing template-free (RAC) and template-based approaches
(BARC, BITE).

我们提出了一种从单目视频中构建可动画的狗头像的方法，通过解决动物的姿态变化和外观问题，提高了基于模板的形状拟合的质量。我们的方法在 CoP3D 和 APTv2 数据集上展示了优异的结果。