Joint-Embedding Prediction of Masked Point Tubes for Self-Supervised Learning on 4D Point Cloud Videos

arXiv:2608.24093 · cs.CV, cs.LG · Submitted 2026-08-25 · Read on arXiv

cs.CV, cs.LG

Submitted: 2026-08-25

Updated: 2026-08-25

Code: https://github.com/Leoxu3/PointTube-JEPA

Terminology

• Ablation — 20× in this paper · explained in 1 episode
• Fine-Tuning — 18× in this paper · explained in 5 episodes
• Accuracy — 17× in this paper · explained in 1 episode
• Self-Supervised Learning — 12× in this paper · explained in 8 episodes
• Regularization — 11× in this paper · explained in 3 episodes
• Transformer — 9× in this paper · explained in 2 episodes
• Latent Space — 8× in this paper · explained in 7 episodes
• Embeddings — 7× in this paper · explained in 2 episodes
• Point Clouds — 7× in this paper · explained in 2 episodes
• Benchmark — 6× in this paper · explained in 2 episodes
• Representation Learning — 6× in this paper · explained in 2 episodes
• Distillation — 5× in this paper · explained in 2 episodes
• Tokenization — 4× in this paper · explained in 2 episodes
• Transfer Learning — 4× in this paper · explained in 10 episodes
• Alignment — 3× in this paper · explained in 1 episode
• Conference on Computer Vision and Pattern Recognition — 3× in this paper
• Joint-Embedding Predictive Architectures — 3× in this paper
• Latent Representation — 3× in this paper · explained in 2 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• CVF International Conference on Computer Vision — 2× in this paper
• Clustering — 2× in this paper · explained in 2 episodes
• Few-Shot Learning — 2× in this paper · explained in 4 episodes
• Latent Representations — 2× in this paper · explained in 3 episodes
• Semi-supervised Learning — 2× in this paper · explained in 4 episodes
• Winter Conference on Applications of Computer Vision — 2× in this paper
• Ablation Studies — 1× in this paper · explained in 1 episode
• Consistency — 1× in this paper · explained in 3 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Distribution Matching — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Robustness — 1× in this paper · explained in 5 episodes

Sources

• LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics
• 3D-JEPA: A Joint Embedding Predictive Architecture for 3D Self-Supervised Representation Learning
• STS-Mixer: Spatio-Temporal-Spectral Mixer for 4D Point Cloud Video Understanding
• LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels
• Cross4D-JEPA: Dense Cross-modal Correspondence Distillation for 4D Point Cloud Representation Learning
• Diffusion Masked Pretraining for Dynamic Point Cloud
• Uni4D: A Unified Self-Supervised Learning Framework for Point Cloud Videos

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