Aided by text-to-image and text-to-video diffusion models, existing 4D
content creation pipelines utilize score distillation sampling to optimize the
entire dynamic 3D scene. However, as these pipelines generate 4D content from
text or image inputs, they incur significant time and effort in prompt
engineering through trial and error. This work introduces 4DGen, a novel,
holistic framework for grounded 4D content creation that decomposes the 4D
generation task into multiple stages. We identify static 3D assets and
monocular video sequences as key components in constructing the 4D content. Our
pipeline facilitates conditional 4D generation, enabling users to specify
geometry (3D assets) and motion (monocular videos), thus offering superior
control over content creation. Furthermore, we construct our 4D representation
using dynamic 3D Gaussians, which permits efficient, high-resolution
supervision through rendering during training, thereby facilitating
high-quality 4D generation. Additionally, we employ spatial-temporal pseudo
labels on anchor frames, along with seamless consistency priors implemented
through 3D-aware score distillation sampling and smoothness regularizations.
Compared to existing baselines, our approach yields competitive results in
faithfully reconstructing input signals and realistically inferring renderings
from novel viewpoints and timesteps. Most importantly, our method supports
grounded generation, offering users enhanced control, a feature difficult to
achieve with previous methods. Project page:
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4DGen 是一种新颖的综合框架，将 4D 内容创建任务拆分为多个阶段，利用静态 3D 资产和单目视频序列作为构建 4D 内容的关键组成部分，使用动态 3D Gaussians 构建 4D 表示，在训练期间利用渲染进行高分辨率监督，从而实现了高质量的 4D 生成，并实施了 3D 感知的得分蒸馏采样和平滑正则化以提供空间 - 时间伪标签和无缝一致性先验，相比现有基线方法，在忠实重建输入信号和从新视点和时间步骤推断渲染方面取得了具有竞争力的结果，最重要的是，我们的方法支持基于实际场景的生成，为用户提供了增强控制，这是以前方法难以实现的特点。