AnyView: Synthesizing Any Novel View in Dynamic Scenes

arXiv:2601.16982 · cs.CV, cs.LG, cs.RO · Submitted 2026-01-23 · Read on arXiv

cs.CV, cs.LG, cs.RO

Submitted: 2026-01-23

Updated: 2026-09-10

Comments: Project webpage: https://tri-ml.github.io/AnyView/

Code: https://github.com/nvidia-cosmos/cosmospredict2

Project page: https://tri-ml.github.io/AnyView

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Benchmark — 23× in this paper · explained in 2 episodes
• Diffusion Model — 12× in this paper · explained in 8 episodes
• Diffusion Models — 10× in this paper · explained in 6 episodes
• Consistency — 9× in this paper · explained in 3 episodes
• Computer Vision and Pattern Recognition — 7× in this paper
• Foundation Model — 6× in this paper · explained in 3 episodes
• Transformer — 6× in this paper · explained in 2 episodes
• Adam — 5× in this paper · explained in 2 episodes
• Foundation Models — 5× in this paper · explained in 7 episodes
• Generative Model — 5× in this paper · explained in 2 episodes
• World Model — 5× in this paper · explained in 7 episodes
• Fidelity — 4× in this paper · explained in 2 episodes
• Generative Models — 4× in this paper · explained in 2 episodes
• Accuracy — 3× in this paper · explained in 1 episode
• Calibration — 3× in this paper · explained in 7 episodes
• Embeddings — 3× in this paper · explained in 2 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Generative Camera Dolly — 2× in this paper
• Inverse Problem — 2× in this paper · explained in 2 episodes
• Temporal consistency — 2× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Amodal Completion — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 3 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Distribution Shift — 1× in this paper · explained in 6 episodes
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Gaussian Splatting — 1× in this paper · explained in 4 episodes
• Hybrid Approach — 1× in this paper · explained in 4 episodes
• In Conference on Robot Learning — 1× in this paper
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Mixture of Experts — 1× in this paper · explained in 2 episodes
• Optical Flow — 1× in this paper · explained in 2 episodes
• Point Clouds — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes

Sources

• Cosmos World Foundation Model Platform for Physical AI
• ReCamMaster: Camera-Controlled Generative Rendering from A Single Video
• Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets
• Dreamitate: Real-World Visuomotor Policy Learning via Video Generation
• CameraCtrl: Enabling Camera Control for Text-to-Video Generation
• View-Invariant Policy Learning via Zero-Shot Novel View Synthesis
• Wan: Open and Advanced Large-Scale Video Generative Models
• Mobi-$\pi$: Mobilizing Your Robot Learning Policy
• Depth Anything V2
• CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer
• ReCapture: Generative Video Camera Controls for User-Provided Videos using Masked Video Fine-Tuning
• CamI2V: Camera-Controlled Image-to-Video Diffusion Model

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