mlsanityData to 7 Oct 2026

SLAMP: Stochastic Latent Appearance and Motion Prediction

arXiv 2108.02760 ↗First posted 5 Aug 2021cs.CVcited by 32

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Abstract (arXiv)

Motion is an important cue for video prediction and often utilized by separating video content into static and dynamic components. Most of the previous work utilizing motion is deterministic but there are stochastic methods that can model the inherent uncertainty of the future. Existing stochastic models either do not reason about motion explicitly or make limiting assumptions about the static part. In this paper, we reason about appearance and motion in the video stochastically by predicting the future based on the motion history. Explicit reasoning about motion without history already reaches the performance of current stochastic models. The motion history further improves the results by allowing to predict consistent dynamics several frames into the future. Our model performs comparably to the state-of-the-art models on the generic video prediction datasets, however, significantly outperforms them on two challenging real-world autonomous driving datasets with complex motion and dynamic background.

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). Builds on 31 of its 38 references that are in the map. First posted on arXiv 5 Aug 2021.

Builds on

31 of 38 references.

References · 31

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19 more
  1. 2016Unsupervised Learning for Physical Interaction through Video Predictioncited by 256
  2. 2018Stochastic Video Generation with a Learned Priorcited by 244
  3. 2017Stochastic Variational Video Predictioncited by 234
  4. 2017Video Frame Synthesis using Deep Voxel Flowcited by 209
  5. 2016Deep Predictive Coding Networks for Video Prediction and Unsupervised Learningcited by 203
  6. 2018Stochastic Adversarial Video Predictioncited by 173
  7. 2017Self-Supervised Visual Planning with Temporal Skip Connectionscited by 137
  8. 2018End-to-end Structure-Aware Convolutional Networks for Knowledge Base Completioncited by 136
  9. 2020Stochastic Latent Residual Video Predictioncited by 106
  10. 2019Improved Conditional VRNNs for Video Predictioncited by 104
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  12. 2019High Fidelity Video Prediction with Large Stochastic Recurrent Neural Networkscited by 88
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  14. 2017Dual Motion GAN for Future-Flow Embedded Video Predictioncited by 69
  15. 2018Flow-Grounded Spatial-Temporal Video Prediction from Still Imagescited by 54
  16. 2018Temporal Difference Variational Auto-Encodercited by 48
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Citing papers per year: 2022: 13; 2023: 6; 2024: 7; 2025: 5; 2026: 1.

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  1. 2022MCVD: Masked Conditional Video Diffusion for Prediction, Generation, and Interpolationcited by 307
  2. 2022Diffusion Probabilistic Modeling for Video Generationcited by 237
  3. 2022MaskViT: Masked Visual Pre-Training for Video Predictioncited by 112
  4. 2022InfiniteNature-Zero: Learning Perpetual View Generation of Natural Scenes from Single Imagescited by 82
  5. 2022Efficient Video Prediction via Sparsely Conditioned Flow Matchingcited by 54
  6. 2023A Dynamic Multi-Scale Voxel Flow Network for Video Predictioncited by 53
  7. 2022StretchBEV: Stretching Future Instance Prediction Spatially and Temporallycited by 47
  8. 2022Show Me What and Tell Me How: Video Synthesis via Multimodal Conditioningcited by 37
  9. 2023Video Diffusion Models with Local-Global Context Guidancecited by 15
  10. 2023Implicit Stacked Autoregressive Model for Video Predictioncited by 14
  11. 2025Slot-Guided Adaptation of Pre-trained Diffusion Models for Object-Centric Learning and Compositional Generationcited by 13
  12. 2022MSPred: Video Prediction at Multiple Spatio-Temporal Scales with Hierarchical Recurrent Networkscited by 12
20 more
  1. 2023MOSO: Decomposing MOtion, Scene and Object for Video Predictioncited by 12
  2. 2026Improving Flow Matching by Aligning Flow Divergencecited by 9
  3. 2022Stochastic Video Prediction with Structure and Motioncited by 8
  4. 2024A Survey on Future Frame Synthesis: Bridging Deterministic and Generative Approachescited by 7
  5. 2022MS-RNN: A Flexible Multi-Scale Framework for Spatiotemporal Predictive Learningcited by 5
  6. 2022WALDO: Future Video Synthesis using Object Layer Decomposition and Parametric Flow Predictioncited by 5
  7. 2023MS-LSTM: Exploring Spatiotemporal Multiscale Representations in Video Prediction Domaincited by 4
  8. 2024Artificial Intelligence for Biomedical Video Generationcited by 2
  9. 2022Disentangling Content and Motion for Text-Based Neural Video Manipulationcited by 1
  10. 2023TKN: Transformer-based Keypoint Prediction Network For Real-time Video Predictioncited by 1
  11. 2024Masked Autoregressive Model for Weather Forecastingcited by 1
  12. 2024Continuous Video Process: Modeling Videos as Continuous Multi-Dimensional Processes for Video Predictioncited by 1
  13. 2025Real-time Video Prediction With Fast Video Interpolation Model and Prediction Trainingcited by 1
  14. 2022Motion and Context-Aware Audio-Visual Conditioned Video Predictioncited by 0
  15. 2024State-space Decomposition Model for Video Prediction Considering Long-term Motion Trendcited by 0
  16. 2024Lightweight Stochastic Video Prediction via Hybrid Warpingcited by 0
  17. 2024Efficient Continuous Video Flow Model for Video Predictioncited by 0
  18. 2025Resolving Spatio-Temporal Entanglement in Video Prediction via Multi-Modal Attentioncited by 0
  19. 2025Compositional Video Synthesis by Temporal Object-Centric Learningcited by 0
  20. 2025Sequence-Adaptive Video Prediction in Continuous Streams using Diffusion Noise Optimizationcited by 0