DynaTokens: Teaching Dynamics to Camera-Controlled Video Models at Test Time

arXiv:2609.35704 · cs.CV · Submitted 2026-09-28 · Read on arXiv

cs.CV

Submitted: 2026-09-28

Updated: 2026-09-28

Project page: https://glab-caltech.github.io/dynatokens

Terminology

• Test-time Training — 33× in this paper
• World Model — 19× in this paper · explained in 7 episodes
• Benchmark — 12× in this paper · explained in 2 episodes
• Dynamic Spatial Relation — 8× in this paper
• Accuracy — 7× in this paper · explained in 1 episode
• Motion Accuracy — 7× in this paper
• Adaptation — 6× in this paper · explained in 1 episode
• Transformer — 6× in this paper · explained in 2 episodes
• Ablation — 5× in this paper · explained in 1 episode
• Fine-Tuning — 5× in this paper · explained in 5 episodes
• Motion Order Understanding — 4× in this paper
• Diffusion Model — 3× in this paper · explained in 8 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• In International Conference on Learning Representations — 3× in this paper
• Alignment — 2× in this paper · explained in 1 episode
• Diffusion Models — 2× in this paper · explained in 6 episodes
• Entanglement — 2× in this paper · explained in 3 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Visual Prompt Tuning — 2× in this paper
• Adam — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Exposure Bias — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Optical Flow — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Self-distillation — 1× in this paper · explained in 6 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Out of Sight but Not Out of Mind: Hybrid Memory for Dynamic Video World Models
• LiveWorld: Simulating Out-of-Sight Dynamics in Generative Video World Models
• ViPE: Video Pose Engine for 3D Geometric Perception
• Out of Sight, Out of Mind? Evaluating State Evolution in Video World Models
• Lyra 2.0: Explorable Generative 3D Worlds
• Kling-Omni Technical Report
• Advancing Open-source World Models
• BulletTime: Decoupled Control of Time and Camera Pose for Video Generation
• MosaicMem: Hybrid Spatial Memory for Controllable Video World Models
• Learning to Discover at Test Time
• VBench-2.0: Advancing Video Generation Benchmark Suite for Intrinsic Faithfulness

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models