Understanding the compositional dynamics of the world in unsupervised 3D
scenarios is challenging. Existing approaches either fail to make effective use
of time cues or ignore the multi-view consistency of scene decomposition. In
this paper, we propose DynaVol, an inverse neural rendering framework that
provides a pilot study for learning time-varying volumetric representations for
dynamic scenes with multiple entities (like objects). It has two main
contributions. First, it maintains a time-dependent 3D grid, which dynamically
and flexibly binds the spatial locations to different entities, thus
encouraging the separation of information at a representational level. Second,
our approach jointly learns grid-level local dynamics, object-level global
dynamics, and the compositional neural radiance fields in an end-to-end
architecture, thereby enhancing the spatiotemporal consistency of
object-centric scene voxelization. We present a two-stage training scheme for
DynaVol and validate its effectiveness on various benchmarks with multiple
objects, diverse dynamics, and real-world shapes and textures. We present
visualization at this https URL

本文提出了 DynaVol，一种逆神经渲染框架，用于学习动态场景中多实体的时变体积表示，并在多个基准测试中通过两阶段训练验证了其有效性。