Object-Centric Diffusion Models

• Object-centric diffusion models are generative frameworks that structure scene representations into discrete object-level embeddings for enhanced compositionality.
• They integrate slot attention with diffusion processes to improve segmentation, instance editing, and zero-shot generalization across high-dimensional domains.
• Applications span images, 3D rendering, and robotic manipulation, offering efficient, interpretable, and controllable outputs while addressing challenges like part-whole ambiguity.

Object-centric diffusion models are a class of generative and predictive models that explicitly structure their internal representations around discrete object entities or entities (“slots”), leveraging the diffusion modeling paradigm to enhance decomposition, generation, reasoning, and manipulation of complex scenes, data, or behaviors. Unlike traditional diffusion models that treat the scene as a monolithic array of pixels or feature vectors, object-centric diffusion models integrate inductive biases or architectural components that enable object-level structuring, facilitating compositionality, systematic generalization, and controllable outputs in high-dimensional domains such as images, 3D shapes, videos, and action sequences.

1. Foundations and Distinction from Conventional Diffusion Models

Traditional diffusion models operate by mapping a complex high-dimensional data distribution (such as natural images) to a simple prior via a Markov chain of gradual noise addition (forward process), and learning a reverse process to convert noise back into realistic samples. The fundamental mathematical framework (Section 2 of (2312.10393)) involves iterative updates: $$q(x_{t}|x_{t-1}) = \mathcal{N}(x_{t}; \sqrt{1-\beta_t} x_{t-1}, \beta_t\mathbf{I})$$ with reverse denoising via parameterized neural networks.

Object-centric diffusion models introduce explicit decomposition—representing inputs as sets of object-level embeddings (often “slots” via attention mechanisms)—and condition the denoising process on these representations or generate structured outputs directly at the object level. This enables more flexible, compositional, and interpretable modeling of scenes and facilitates downstream tasks such as object discovery, instance editing, and zero-shot generalization. Methods such as Latent Slot Diffusion (2303.10834), SlotDiffusion (2305.11281), and SlotAdapt (2501.15878) exemplify this paradigm.

2. Architectural Principles and Slot-Based Conditioning

The core architectural innovation across object-centric diffusion models is the integration of slot attention (or similar modules) with conditional diffusion mechanisms:

• Slot Attention maps high-dimensional feature maps into a set of $N$ latent vectors (“slots”), each intended to capture an individual object or region.
• Diffusion Model Integration is achieved by:
  • Conditioning the denoising U-Net (operating in either pixel, latent, or intermediate feature space) on slots, typically via cross-attention layers.
  • In SlotDiffusion and LSD, the slot representations modulate the diffusion U-Net blocks, directly controlling generation at the object level.
  • SlotAdapt (2501.15878) introduces novel adapter cross-attention layers handling slot-object information without overloading text-centric layers, and a guidance loss to align the adapter attention with slot attention for better object binding.
• Auxiliary Mechanisms include additional context tokens (register tokens), language guidance, semantic mask supervision, and adaptive architectural variants for video or 3D scenarios.

This architecture supports tasks requiring object-wise control, compositionality (assembly or modification of scenes from object primitives), and interpretable latent spaces.

3. Modeling and Learning Approaches

Object-centric diffusion models employ variations of the diffusion process tailored to their representation space:

• Latent Diffusion Models (LDMs): Most contemporary frameworks, including LSD and SlotDiffusion, operate in compressed latent spaces produced by pretrained autoencoders or VAEs. This allows efficient and high-fidelity modeling:

  $$\mathbf{z}_t = \sqrt{\bar{\alpha}_t} \mathbf{z}_0 + \sqrt{1 - \bar{\alpha}_t} \epsilon_t, \quad \epsilon_t \sim \mathcal{N}(0,\mathbf{I})$$

  where $\mathbf{z}_0$ is the latent encoding, and conditioning on slots $\mathbf{S}$ is embedded throughout the denoising chain.

• Conditional Generation and Supervision: Slot representations are either supervised with pseudo-masks (as in GLASS (2407.17929), using diffusion-generated semantic masks from pretrained large models), aligned via cross-attention (SlotAdapt), or modulated with compositional or language-based conditions.
• Adapters and Regularizations: Adapter-based cross-attention layers (SlotAdapt) are trained while keeping the base diffusion backbone frozen, avoiding text-centric bias and preserving generation capacity. Guidance losses are added to explicitly align slot attention with downstream cross-attention in the generative process.
• Ensembling and Stochasticity: The inherent stochasticity of diffusion models enables ensemble methods and robustness improvements, as exemplified by OCD (2210.00471), which samples multiple weight deltas for each input and ensembles predictions.

4. Domains of Application

Object-centric diffusion models have demonstrated efficacy and state-of-the-art results across several domains:

• Image and Video Generation: SlotDiffusion and LSD (2305.11281, 2303.10834) set new standards in unsupervised object segmentation, high-fidelity image generation, and compositional editing. These approaches excel at recombining or manipulating scenes by editing slot representations, even against challenging real-world backgrounds (VOC, COCO, FFHQ datasets).
• 3D Neural Rendering and Novel View Synthesis: DORSal (2306.08068) utilizes slot-based scene representations and diffusion models to enable object-level editing and high-fidelity multiview rendering—outperforming deterministic regression baselines in FID, LPIPS, and editability.
• 3D Object Classification and Reasoning: DC3DO (2408.06693) applies diffusion modeling for class-conditional likelihood estimation, enabling robust zero-shot classification and multimodal reasoning for 3D point clouds.
• Manipulation and Planning: EC-Diffuser (2412.18907) and SPOT (2411.00965) apply object-centric diffusion to behavioral cloning, trajectory generation, and manipulation planning, leveraging structured representations for compositional generalization, efficient learning, and cross-embodiment transfer.
• Segmentation and Detection: diffCOD (2308.00303) frames camouflaged object detection as denoising diffusion on masks, with cross-attention to image priors for robust segmentation.
• Collaborative and Perceptual Fusion: CoDiff (2502.14891) employs conditional diffusion in latent space to denoise and fuse multi-agent features for collaborative 3D object detection under noisy real-world conditions.

5. Evaluation, Performance, and Comparative Results

Empirical results across the literature indicate that object-centric diffusion models consistently outperform or complement transformer-based, CNN-based, and GAN-based generative or predictive models:

• Segmentation Metrics (e.g., mIoU, mBO, FG-ARI): SlotDiffusion and SlotAdapt achieve superior instance segmentation on synthetic and real-world benchmarks, resolving chronic part-object confusion.
• Compositional Generation (FID, LPIPS, FVD, SSIM): LSD and SlotDiffusion exhibit lower (better) FID scores, particularly for complex multi-object scenes, and demonstrate slotwise editing, object addition/removal, and compositional control.
• Manipulation and Dynamics: EC-Diffuser and SPOT report improved task success rates and generalization to unseen object configurations, highlighting the benefits of multimodal and structured denoising.
• Efficiency: Slot-based latent world models (2503.06170) operate up to 85% faster in training/inference than diffusion models over high-dimensional pixels, at a minor trade-off in pixel-level realism but major sample and computational gains.

Associated ablations emphasize the importance of correct slot-attention cross-alignment and specialized adapters. Guidance losses and architectural refinements (SlotAdapt's decoupling of text-centric pathways, GLASS's pseudo-mask guidance) have a prominent impact on stability and segmentation completeness, especially for complex, real-world imagery.

6. Current Limitations and Open Challenges

While object-centric diffusion models achieve strong results, several challenges remain:

• Part-Whole Ambiguity: Effectively distinguishing and binding slots to object "wholes" rather than fragments remains challenging, especially in diverse, cluttered settings. Recent developments employ mask guidance (GLASS) or cross-attention alignment (SlotAdapt) to mitigate this, but full resolution is an active area of research.
• Scalability: Extending these methods to handle unconstrained real-world scenes, videos, and demanding tasks such as scalable 3D relationship modeling (2503.19914) or temporal sequence prediction with high object counts, is a prominent ongoing target.
• Efficiency and Interaction: Some approaches (e.g., iterative latent optimization in FlexEdit (2403.18605)) incur higher computation at inference, motivating further work on one-step or accelerated diffusion schemes.
• Conditional and Multimodal Guidance: While text- and language-guided variants exist, reliably binding text to object slots, enabling robust text-to-object editing, and aligning with energy-based or compositional constraints (as in EBAMA (2404.07389)) are still evolving.

7. Emerging Directions and Impact

Research in object-centric diffusion models is expanding to support:

• Compositional 3D scene arrangement and spatial relationship modeling by synthesizing plausible 3D arrangements from 2D diffusion knowledge and training score-based OOR diffusion models (2503.19914).
• Cross-modality and robustness, using semantic labels, captions, or other modalities to supervise slot-object alignment either directly (GLASS) or in a self-supervised manner (SlotAdapt).
• Manipulation and policy learning in robotics by leveraging entity-centric representations and diffusion for behavior generation that generalizes over new object/task combinations (2412.18907, 2411.00965).
• Collaborative perception and sensor fusion wherein conditional diffusion in compressed latent space yields robust, denoised scene representations for multi-agent detection tasks (2502.14891).
• Editing and inpainting frameworks (FlexEdit, DiffOOM) supporting object-centric, mask-adaptive manipulations in both images and videos, with quantifiable improvements in region-specific realism, object placement, and background fidelity.

Object-centric diffusion models are thus converging on a methodology that combines compositional, interpretable, and efficient modeling with the expressive power and flexibility of modern diffusion-based generative methods. This enables a wide array of practical applications and sets new baselines for object-centric learning, structured representation, and controllable generation in vision, graphics, and beyond.