# Causal Deciphering and Inpainting in Spatio-Temporal Dynamics via Diffusion Model

arXiv: https://arxiv.org/abs/2409.19608 · HTML: https://mlsanity.com/paper/2409.19608/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Flow matching, time series and science, one of 14 directions in Diffusion models.
- Builds on 29 of its 106 references that are in the map.
- First posted on arXiv 29 Sep 2024.

## Placed in

- Diffusion models / [Flow matching, time series and science](https://mlsanity.com/diffusion/flow-matching-time-series-and-science/)

## Builds on (most cited first, 29 of 29)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization](https://mlsanity.com/paper/1911.08731/) (2019)
- [In Search of Lost Domain Generalization](https://mlsanity.com/paper/2007.01434/) (2020)
- [DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models](https://mlsanity.com/paper/2211.01095/) (2022)
- [FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators](https://mlsanity.com/paper/2202.11214/) (2022)
- [Transformers in Time Series: A Survey](https://mlsanity.com/paper/2202.07125/) (2022)
- [Deep multi-scale video prediction beyond mean square error](https://mlsanity.com/paper/1511.05440/) (2015)
- [Large Scale Image Completion via Co-Modulated Generative Adversarial Networks](https://mlsanity.com/paper/2103.10428/) (2021)
- [Point-Bind & Point-LLM: Aligning Point Cloud with Multi-modality for 3D Understanding, Generation, and Instruction Following](https://mlsanity.com/paper/2309.00615/) (2023)
- [Pangu-Weather: A 3D High-Resolution Model for Fast and Accurate Global Weather Forecast](https://mlsanity.com/paper/2211.02556/) (2022)
- [Grad-CAM: Why did you say that?](https://mlsanity.com/paper/1611.07450/) (2016)
- [Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook](https://mlsanity.com/paper/2310.10196/) (2023)
- [IMAGDressing-v1: Customizable Virtual Dressing](https://mlsanity.com/paper/2407.12705/) (2024)
- [Boosting Consistency in Story Visualization with Rich-Contextual Conditional Diffusion Models](https://mlsanity.com/paper/2407.02482/) (2024)
- [PastNet: Introducing Physical Inductive Biases for Spatio-temporal Video Prediction](https://mlsanity.com/paper/2305.11421/) (2023)
- [Any2Point: Empowering Any-modality Large Models for Efficient 3D Understanding](https://mlsanity.com/paper/2404.07989/) (2024)
- [Discovering State Variables Hidden in Experimental Data](https://mlsanity.com/paper/2112.10755/) (2021)
- [Deconfounding to Explanation Evaluation in Graph Neural Networks](https://mlsanity.com/paper/2201.08802/) (2022)
- [Two Heads Are Better Than One: Boosting Graph Sparse Training via Semantic and Topological Awareness](https://mlsanity.com/paper/2402.01242/) (2024)
- [CaT-GNN: Enhancing Credit Card Fraud Detection via Causal Temporal Graph Neural Networks](https://mlsanity.com/paper/2402.14708/) (2024)
- [DynST: Dynamic Sparse Training for Resource-Constrained Spatio-Temporal Forecasting](https://mlsanity.com/paper/2403.02914/) (2024)
- [NuwaTS: a Foundation Model Mending Every Incomplete Time Series](https://mlsanity.com/paper/2405.15317/) (2024)
- [Rethinking Urban Mobility Prediction: A Super-Multivariate Time Series Forecasting Approach](https://mlsanity.com/paper/2312.01699/) (2023)

## Built on by (most cited first, 6 of 6)

- [Unraveling Spatio-Temporal Foundation Models via the Pipeline Lens: A Comprehensive Review](https://mlsanity.com/paper/2506.01364/) (2025)
- [Incomplete Data, Complete Dynamics: A Diffusion Approach](https://mlsanity.com/paper/2509.20098/) (2025)
- [Neural Scene Designer: Self-Styled Semantic Image Manipulation](https://mlsanity.com/paper/2509.01405/) (2025)
- [Multi-scale Temporal Prediction via Incremental Generation and Multi-agent Collaboration](https://mlsanity.com/paper/2509.17429/) (2025)
- [A Causal Framework for Mitigating Data Shifts in Healthcare](https://mlsanity.com/paper/2603.13595/) (2026)
- [Inside the Latent Flow: Causal Deciphering of Attention Dynamics in Audio Separation Foundation Models](https://mlsanity.com/paper/2606.10046/) (2026)

## Abstract (arXiv)

Spatio-temporal (ST) prediction has garnered a De facto attention in earth sciences, such as meteorological prediction, human mobility perception. However, the scarcity of data coupled with the high expenses involved in sensor deployment results in notable data imbalances. Furthermore, models that are excessively customized and devoid of causal connections further undermine the generalizability and interpretability. To this end, we establish a causal framework for ST predictions, termed CaPaint, which targets to identify causal regions in data and endow model with causal reasoning ability in a two-stage process. Going beyond this process, we utilize the back-door adjustment to specifically address the sub-regions identified as non-causal in the upstream phase. Specifically, we employ a novel image inpainting technique. By using a fine-tuned unconditional Diffusion Probabilistic Model (DDPM) as the generative prior, we in-fill the masks defined as environmental parts, offering the possibility of reliable extrapolation for potential data distributions. CaPaint overcomes the high complexity dilemma of optimal ST causal discovery models by reducing the data generation complexity from exponential to quasi-linear levels. Extensive experiments conducted on five real-world ST benchmarks demonstrate that integrating the CaPaint concept allows models to achieve improvements ranging from 4.3% to 77.3%. Moreover, compared to traditional mainstream ST augmenters, CaPaint underscores the potential of diffusion models in ST enhancement, offering a novel paradigm for this field. Our project is available at https://anonymous.4open.science/r/12345-DFCC.