Remarkable progress has been made in 4D content generation recently. However,
existing methods suffer from long optimization time, lack of motion
controllability, and a low level of detail. In this paper, we introduce
DreamGaussian4D, an efficient 4D generation framework that builds on 4D
Gaussian Splatting representation. Our key insight is that the explicit
modeling of spatial transformations in Gaussian Splatting makes it more
suitable for the 4D generation setting compared with implicit representations.
DreamGaussian4D reduces the optimization time from several hours to just a few
minutes, allows flexible control of the generated 3D motion, and produces
animated meshes that can be efficiently rendered in 3D engines.

DreamGaussian4D 是一种高效的 4D 生成框架，它建立在 4D 高斯喷洒表示的基础上，通过显式地建模高斯喷洒中的空间变换，提高了 4D 生成的适用性，同时减少了优化时间，增加了对生成的三维动画的灵活控制，产生的动画网格在 3D 引擎中能被高效渲染。

DreamGaussian4D: 生成 4D 高斯飞溅

DreamGaussian4D: Generative 4D Gaussian Splatting

Achieving faster execution with shorter compilation time can enable further
diversity and innovation in neural networks. However, the current paradigm of
executing neural networks either relies on hand-optimized libraries,
traditional compilation heuristics, or very recently, simulated annealing and
genetic algorithms. Our work takes a unique approach by formulating compiler
optimizations for neural networks as a reinforcement learning problem, whose
solution takes fewer steps to converge. This solution, dubbed ReLeASE, comes
with a sampling algorithm that leverages clustering to focus the costly samples
(real hardware measurements) on representative points, subsuming an entire
subspace. Our adaptive sampling not only reduces the number of samples, but
also improves the quality of samples for better exploration in shorter time. As
such, experimentation with real hardware shows that reinforcement learning with
adaptive sampling provides 4.45x speed up in optimization time over AutoTVM,
while also improving inference time of the modern deep networks by 5.6%.
Further experiments also confirm that our adaptive sampling can even improve
AutoTVM's simulated annealing by 4.00x.

通过将神经网络的编译器优化问题作为强化学习问题，本文提出了一种新的解决方案，名为 ReLeASE，它通过聚类来聚焦于代表性点上的昂贵采样，并且通过在较短的时间内更好地探索来提高样本的质量。实验表明，使用自适应采样的强化学习可以大大加快优化时间并提高现代深度网络的推理时间。