Dynamic Scene Understanding through Object-Centric Voxelization and Neural Rendering

🌈 Abstract

The article presents DynaVol-S, a 3D generative model for dynamic scene understanding that learns object-centric representations in an unsupervised manner. The key contributions are:

1. Introducing object-centric voxel representations to capture the 3D nature of the scene and infer per-object occupancy probabilities.
2. Integrating 2D semantic features extracted from large-scale datasets to enhance the model's ability to handle complex real-world scenes.

The proposed approach significantly outperforms existing methods in both novel view synthesis and unsupervised decomposition tasks for dynamic scenes. It also enables additional capabilities like dynamic scene editing through direct manipulation of the learned representations.

🙋 Q&A

[01] Object-Centric Voxelization

1. How does DynaVol-S represent the 3D dynamic scene in an object-centric manner? DynaVol-S uses 4D voxel grids to represent the object-centric occupancy probabilities at individual spatial locations. This allows the model to infer the 3D geometric structure and dynamics of each object in the scene.

2. What are the other voxelized representations used in the model besides the object-centric occupancy probabilities? In addition to the object-centric occupancy probabilities, DynaVol-S also employs voxelized representations for opacity, color-related features, and semantic features of the scene.

3. How does DynaVol-S leverage the object-centric latent codes in the neural renderer? The object-centric latent codes are used as inputs to the compositional NeRF renderer, which learns linear combinations of the latent codes to construct the object-specific projections for rendering.

[02] Semantic Volume Slot Attention

1. What is the purpose of the semantic volume slot attention module in DynaVol-S? The semantic volume slot attention module aims to correlate the object-centric voxel grids with pre-learned 2D semantic feature maps, enabling the model to better interpret complex real-world scenes.

2. How does the module extract and integrate the semantic features into the 3D voxel representations? The module uses a pre-trained DINOv2 network to extract 2D semantic features, which are then projected onto the 3D voxel grids using a volume slot attention mechanism.

3. What are the benefits of incorporating the semantic features in DynaVol-S compared to the previous DynaVol model? Incorporating semantic features significantly improves the model's performance in handling complex real-world scenes, where the increased complexity of color and geometry patterns, as well as limited view directions, pose challenges.

[03] Dynamic Scene Editing

1. What are the key capabilities of DynaVol-S that enable dynamic scene editing? After training, the object-centric voxel representations learned by DynaVol-S can be directly manipulated to perform various scene editing tasks, such as object removal, replacement, and trajectory modification, without the need for further training.

2. How does DynaVol-S achieve this flexibility in scene editing? The explicit and meaningful voxel-based representations, along with the learned deformation function, allow users to directly modify the scene by manipulating the object occupancy values or switching the deformation function to a pre-defined one.

3. What are some examples of scene editing demonstrated in the paper? The paper showcases examples of removing objects, modifying object dynamics (e.g., from falling to rotating), and swapping object colors in the real-world scenes.