InJecteD: Analyzing Trajectories and Drift Dynamics in Denoising Diffusion Probabilistic Models for 2D Point Cloud Generation

2025-09-09

AI Summary

This work addresses the black-box nature of the denoising process in Denoising Diffusion Probabilistic Models (DDPMs) for 2D point cloud generation. We propose InJecteD, the first framework to systematically quantify trajectory dynamics—including displacement, velocity, clustering structure, and drift field evolution—during denoising. By simplifying the DDPM architecture and integrating Fourier time embeddings with adaptive noise scheduling, we analyze trajectory evolution across four configuration variants using Wasserstein distance and cosine similarity. Experiments uncover dataset-specific denoising mechanisms: e.g., bullseye exhibits concentric convergence, while dino demonstrates progressive contour formation. Fourier time embeddings significantly improve trajectory stability and reconstruction fidelity. Our approach enhances the interpretability of diffusion models by establishing a quantitative, analyzable paradigm for human-in-the-loop debugging and generative process control.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Web Mining and Content Analysis: Content-based information diffusion

Abstract

This work introduces InJecteD, a framework for interpreting Denoising Diffusion Probabilistic Models (DDPMs) by analyzing sample trajectories during the denoising process of 2D point cloud generation. We apply this framework to three datasets from the Datasaurus Dozen bullseye, dino, and circle using a simplified DDPM architecture with customizable input and time embeddings. Our approach quantifies trajectory properties, including displacement, velocity, clustering, and drift field dynamics, using statistical metrics such as Wasserstein distance and cosine similarity. By enhancing model transparency, InJecteD supports human AI collaboration by enabling practitioners to debug and refine generative models. Experiments reveal distinct denoising phases: initial noise exploration, rapid shape formation, and final refinement, with dataset-specific behaviors example, bullseyes concentric convergence vs. dinos complex contour formation. We evaluate four model configurations, varying embeddings and noise schedules, demonstrating that Fourier based embeddings improve trajectory stability and reconstruction quality

Problem

• Analyzing denoising trajectories in 2D point cloud diffusion models
• Quantifying trajectory dynamics through statistical metrics and drift analysis
• Enhancing model transparency for debugging and refining generative AI

Innovation

• Analyzing denoising trajectories using statistical metrics
• Customizable Fourier embeddings for improved stability
• Quantifying drift dynamics with Wasserstein distance