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Structured Generative Models for Scene Understanding

Christopher K. I. Williams

TL;DR

This paper advocates structured generative models (SGMs) as a principled framework for static scene understanding, emphasizing object-centric representations and scene-level relations that enable coherent, editable 3D reconstructions from images. It surveys object models (things vs. stuff), scene models (autoregressive, energy-based, hierarchical grammars), and inference strategies (MCMC, variational methods, differentiable rendering), highlighting the benefits of compositionality, interpretability, and multi-task transfer. The authors discuss strengths and limitations of SGMs, the open-world challenges, and the need for datasets and benchmarks to study 3D scene inference, editing, and completion. They conclude with a roadmap for advancing the field, including richer object/scene models, scalable inference, and standardized evaluation toward end-to-end SGM-enabled scene understanding.

Abstract

This position paper argues for the use of \emph{structured generative models} (SGMs) for the understanding of static scenes. This requires the reconstruction of a 3D scene from an input image (or a set of multi-view images), whereby the contents of the image(s) are causally explained in terms of models of instantiated objects, each with their own type, shape, appearance and pose, along with global variables like scene lighting and camera parameters. This approach also requires scene models which account for the co-occurrences and inter-relationships of objects in a scene. The SGM approach has the merits that it is compositional and generative, which lead to interpretability and editability. \\\\ To pursue the SGM agenda, we need models for objects and scenes, and approaches to carry out inference. We first review models for objects, which include ``things'' (object categories that have a well defined shape), and ``stuff'' (categories which have amorphous spatial extent). We then move on to review \emph{scene models} which describe the inter-relationships of objects. Perhaps the most challenging problem for SGMs is \emph{inference} of the objects, lighting and camera parameters, and scene inter-relationships from input consisting of a single or multiple images. We conclude with a discussion of issues that need addressing to advance the SGM agenda.

Structured Generative Models for Scene Understanding

TL;DR

This paper advocates structured generative models (SGMs) as a principled framework for static scene understanding, emphasizing object-centric representations and scene-level relations that enable coherent, editable 3D reconstructions from images. It surveys object models (things vs. stuff), scene models (autoregressive, energy-based, hierarchical grammars), and inference strategies (MCMC, variational methods, differentiable rendering), highlighting the benefits of compositionality, interpretability, and multi-task transfer. The authors discuss strengths and limitations of SGMs, the open-world challenges, and the need for datasets and benchmarks to study 3D scene inference, editing, and completion. They conclude with a roadmap for advancing the field, including richer object/scene models, scalable inference, and standardized evaluation toward end-to-end SGM-enabled scene understanding.

Abstract

This position paper argues for the use of \emph{structured generative models} (SGMs) for the understanding of static scenes. This requires the reconstruction of a 3D scene from an input image (or a set of multi-view images), whereby the contents of the image(s) are causally explained in terms of models of instantiated objects, each with their own type, shape, appearance and pose, along with global variables like scene lighting and camera parameters. This approach also requires scene models which account for the co-occurrences and inter-relationships of objects in a scene. The SGM approach has the merits that it is compositional and generative, which lead to interpretability and editability. \\\\ To pursue the SGM agenda, we need models for objects and scenes, and approaches to carry out inference. We first review models for objects, which include ``things'' (object categories that have a well defined shape), and ``stuff'' (categories which have amorphous spatial extent). We then move on to review \emph{scene models} which describe the inter-relationships of objects. Perhaps the most challenging problem for SGMs is \emph{inference} of the objects, lighting and camera parameters, and scene inter-relationships from input consisting of a single or multiple images. We conclude with a discussion of issues that need addressing to advance the SGM agenda.
Paper Structure (34 sections, 10 equations, 10 figures)

This paper contains 34 sections, 10 equations, 10 figures.

Table of Contents

  1. Introduction
  2. Scene Understanding
  3. Alternative scene representations
  4. General Advantages of Generative Models
  5. Pattern synthesis (unconditional generation):
  6. Imputation and restoration (conditional generation):
  7. Anomaly detection:
  8. Data compression:
  9. Pros and Cons of Structured Generative Models
  10. Is full inference overkill?
  11. Models of Objects
  12. Modelling Things: Multifactor Models
  13. Example: Blanz and Vetter's morphable model of faces.
  14. Example: CodeNeRF models disentangled Neural Radiance Fields for object categories.
  15. Other multifactor models.
  16. ...and 19 more sections

Figures (10)

  • Figure 1: The input image (left) is explained in terms of 3D objects, the camera pose and illumination, to produce the reconstructed image (right). Images from romaszko-williams-winn-20.
  • Figure 2: The left most panel shows two frames from a video of two people walking past each other against a background. The second panel shows the mask (top) and appearance of the first sprite learned. The third panel shows the same thing for the second sprite. The rightmost panel shows the learned background. Images from williams-titsias-04.
  • Figure 3: Two example images 2008_001062 and 2008_000043 from the PASCAL VOC 2008 dataset.
  • Figure 4: Image inpainting task, with the green rectangle blanked out, based on image 2008_000959 from the PASCAL VOC 2008 dataset.
  • Figure 5: Images of an office scene from two viewpoints.
  • ...and 5 more figures