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On the Benefits of Instance Decomposition in Video Prediction Models

Eliyas Suleyman, Paul Henderson, Nicolas Pugeault

TL;DR

This work tackles the challenge of predicting future video frames by explicitly decomposing a dynamic scene into object-centric latent slots within a latent-transformer framework. It introduces an object-aware autoencoder (OAAE) with class-specific encoders and codebooks and couples it to a multi-object transformer that uses instance-level self- and cross-attention to model both individual dynamics and inter-object interactions. Controlled experiments across synthetic and real datasets show that object decomposition, coupled with cross-attention, yields higher-quality predictions at equal capacity, especially in scenes with strong interactions. The approach demonstrates the practical benefits of object-centric modeling for scalable video prediction and provides design guidance for future latent-transformer architectures.

Abstract

Video prediction is a crucial task for intelligent agents such as robots and autonomous vehicles, since it enables them to anticipate and act early on time-critical incidents. State-of-the-art video prediction methods typically model the dynamics of a scene jointly and implicitly, without any explicit decomposition into separate objects. This is challenging and potentially sub-optimal, as every object in a dynamic scene has their own pattern of movement, typically somewhat independent of others. In this paper, we investigate the benefit of explicitly modeling the objects in a dynamic scene separately within the context of latent-transformer video prediction models. We conduct detailed and carefully-controlled experiments on both synthetic and real-world datasets; our results show that decomposing a dynamic scene leads to higher quality predictions compared with models of a similar capacity that lack such decomposition.

On the Benefits of Instance Decomposition in Video Prediction Models

TL;DR

This work tackles the challenge of predicting future video frames by explicitly decomposing a dynamic scene into object-centric latent slots within a latent-transformer framework. It introduces an object-aware autoencoder (OAAE) with class-specific encoders and codebooks and couples it to a multi-object transformer that uses instance-level self- and cross-attention to model both individual dynamics and inter-object interactions. Controlled experiments across synthetic and real datasets show that object decomposition, coupled with cross-attention, yields higher-quality predictions at equal capacity, especially in scenes with strong interactions. The approach demonstrates the practical benefits of object-centric modeling for scalable video prediction and provides design guidance for future latent-transformer architectures.

Abstract

Video prediction is a crucial task for intelligent agents such as robots and autonomous vehicles, since it enables them to anticipate and act early on time-critical incidents. State-of-the-art video prediction methods typically model the dynamics of a scene jointly and implicitly, without any explicit decomposition into separate objects. This is challenging and potentially sub-optimal, as every object in a dynamic scene has their own pattern of movement, typically somewhat independent of others. In this paper, we investigate the benefit of explicitly modeling the objects in a dynamic scene separately within the context of latent-transformer video prediction models. We conduct detailed and carefully-controlled experiments on both synthetic and real-world datasets; our results show that decomposing a dynamic scene leads to higher quality predictions compared with models of a similar capacity that lack such decomposition.
Paper Structure (26 sections, 9 equations, 32 figures, 6 tables)

This paper contains 26 sections, 9 equations, 32 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Recurrent models for video prediction
  4. Transformer models for video prediction
  5. Diffusion models for video prediction
  6. Object-centric video prediction
  7. Cross-attention
  8. Methodology
  9. Object-aware autoencoder
  10. Instance decomposition
  11. Instance embedding
  12. Loss function
  13. Variations of the OAAE
  14. Prediction Model
  15. Spatial and temporal extensions of attention layers
  16. ...and 11 more sections

Figures (32)

  • Figure 1: Top: Our proposed multi-object interacting model SCAT. First the input frames are decomposed via a segmentation model, then each decomposed sequence passes through class-specific encoder to convert the 2D frames into latent representations; then class-specific transformer blocks learn and predict the dynamics of each instance and its relationships with other instances in latent space; lastly, the predicted latent representation are decoded via joint decoder to reconstruct the predicted RGB frames. Bottom: The non-decomposed single-slot variant SiS where the scene is modeled globally and jointly.
  • Figure 2: Left: Architecture of the multi-object latent transformer. Right: Detail of spatial and temporal attention blocks.
  • Figure 3: Comparison of different model variants on the Kubric-Real dataset. SCAT successfully predicted that the blue pot bounced away whereas SNCAT neglected the interaction between other objects and let the blue pot go through from other objects. The single-slot model SiS fails to capture the appearances well, yielding indistinct predictions for later frames.
  • Figure 4: Qualitative results from our full model and baselines on KTH (left), Real-Traffic (middle) and Kubric-real (right).
  • Figure 5: Comparison of Mean LPIPS Values on Real-Traffic datasets
  • ...and 27 more figures