mlsanityData to 14 Oct 2026

ProDiff: Prototype-Guided Diffusion for Minimal Information Trajectory Imputation

arXiv 2505.23048 ↗First posted 29 May 2025cs.LGcited by 0

On the map: Diffusion models → Diffusion and flow policies for robotics

Abstract (arXiv)

Trajectory data is crucial for various applications but often suffers from incompleteness due to device limitations and diverse collection scenarios. Existing imputation methods rely on sparse trajectory or travel information, such as velocity, to infer missing points. However, these approaches assume that sparse trajectories retain essential behavioral patterns, which place significant demands on data acquisition and overlook the potential of large-scale human trajectory embeddings. To address this, we propose ProDiff, a trajectory imputation framework that uses only two endpoints as minimal information. It integrates prototype learning to embed human movement patterns and a denoising diffusion probabilistic model for robust spatiotemporal reconstruction. Joint training with a tailored loss function ensures effective imputation. ProDiff outperforms state-of-the-art methods, improving accuracy by 6.28% on FourSquare and 2.52% on WuXi. Further analysis shows a 0.927 correlation between generated and real trajectories, demonstrating the effectiveness of our approach.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Diffusion and flow policies for robotics, one of 14 directions in Diffusion models. Builds on 24 of its 59 references that are in the map. First posted on arXiv 29 May 2025.

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24 of 59 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024ControlTraj: Controllable Trajectory Generation with Topology-Constrained Diffusion Model23 Apr

Other references · 23

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2020Denoising Diffusion Implicit Modelscited by 9,781
  3. 2022Generative Adversarial Networkscited by 5,627
  4. 2020Transformers are RNNs: Fast Autoregressive Transformers with Linear Attentioncited by 2,022
  5. 2022TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysiscited by 1,058
  6. 2016Recurrent Neural Networks for Multivariate Time Series with Missing Valuescited by 554
  7. 2019A Theoretical Analysis of Contrastive Unsupervised Representation Learningcited by 530
  8. 2016Tutorial on Variational Autoencoderscited by 409
  9. 2024Diffusion-TS: Interpretable Diffusion for General Time Series Generationcited by 184
  10. 2022SAITS: Self-Attention-based Imputation for Time Seriescited by 169
  11. 2023PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Predictioncited by 137
  12. 2022Spatio-Temporal Self-Supervised Learning for Traffic Flow Predictioncited by 77
11 more
  1. 2020Deep Gravity: enhancing mobility flows generation with deep neural networks and geographic informationcited by 58
  2. 2023Uncovering the Missing Pattern: Unified Framework Towards Trajectory Imputation and Predictioncited by 47
  3. 2023Continuous Trajectory Generation Based on Two-Stage GANcited by 37
  4. 2024AirPhyNet: Harnessing Physics-Guided Neural Networks for Air Quality Predictioncited by 26
  5. 2019CDSA: Cross-Dimensional Self-Attention for Multivariate, Geo-tagged Time Series Imputationcited by 24
  6. 2024Trajectory Data Management and Mining: A Survey from Deep Learning to the LLM Eracited by 23
  7. 2022RNTrajRec: Road Network Enhanced Trajectory Recovery with Spatial-Temporal Transformercited by 21
  8. 2024Future Directions in Human Mobility Sciencecited by 16
  9. 2021AttnMove: History Enhanced Trajectory Recovery via Attentional Networkcited by 11
  10. 2024Micro-Macro Spatial-Temporal Graph-based Encoder-Decoder for Map-Constrained Trajectory Recoverycited by 9
  11. 2024Full Bayesian Significance Testing for Neural Networkscited by 8

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