CameraCtrl II: Dynamic Scene Exploration via Camera-controlled Video Diffusion Models
On the map: Diffusion models → Video generation
Abstract (arXiv)
This paper introduces CameraCtrl II, a framework that enables large-scale dynamic scene exploration through a camera-controlled video diffusion model. Previous camera-conditioned video generative models suffer from diminished video dynamics and limited range of viewpoints when generating videos with large camera movement. We take an approach that progressively expands the generation of dynamic scenes -- first enhancing dynamic content within individual video clip, then extending this capability to create seamless explorations across broad viewpoint ranges. Specifically, we construct a dataset featuring a large degree of dynamics with camera parameter annotations for training while designing a lightweight camera injection module and training scheme to preserve dynamics of the pretrained models. Building on these improved single-clip techniques, we enable extended scene exploration by allowing users to iteratively specify camera trajectories for generating coherent video sequences. Experiments across diverse scenarios demonstrate that CameraCtrl Ii enables camera-controlled dynamic scene synthesis with substantially wider spatial exploration than previous approaches.
Key facts. Cited by 98 papers in the map (data through 14 Oct 2026). Placed in Video generation, one of 14 directions in Diffusion models. Builds on 64 of its 75 references that are in the map. First posted on arXiv 13 Mar 2025.
Builds on
64 of 75 references.
Closest ancestors · 20
Same direction, newest first
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- 2024VD3D: Taming Large Video Diffusion Transformers for 3D Camera Control17 Jul
- 2024Training-free Camera Control for Video Generation14 Jun
- 2024Vivid-ZOO: Multi-View Video Generation with Diffusion Model12 Jun
- 2024CamCo: Camera-Controllable 3D-Consistent Image-to-Video Generation4 Jun
- 2024Collaborative Video Diffusion: Consistent Multi-video Generation with Camera Control27 May
- 2024CameraCtrl: Enabling Camera Control for Text-to-Video Generation2 Apr
8 more
- 2024Latte: Latent Diffusion Transformer for Video Generation5 Jan
- 2023Photorealistic Video Generation with Diffusion Models11 Dec
- 2023SparseCtrl: Adding Sparse Controls to Text-to-Video Diffusion Models28 Nov
- 2023Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets25 Nov
- 2023Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation9 Oct
- 2023AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning10 Jul
- 2023Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models18 Apr
- 2022Video Diffusion Models7 Apr
Other references · 44
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- 2022Elucidating the Design Space of Diffusion-Based Generative Modelscited by 3,045
- 2020RAFT: Recurrent All-Pairs Field Transforms for Optical Flowcited by 2,415
- 2023VBench: Comprehensive Benchmark Suite for Video Generative Modelscited by 1,822
- 2022Make-A-Video: Text-to-Video Generation without Text-Video Datacited by 1,742
- 2018Towards Accurate Generative Models of Video: A New Metric & Challengescited by 1,234
- 2021Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrievalcited by 1,177
- 2022CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformerscited by 985
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- 2023DL3DV-10K: A Large-Scale Scene Dataset for Deep Learning-based 3D Visioncited by 537
- 2024CAT3D: Create Anything in 3D with Multi-View Diffusion Modelscited by 432
- 2024Depth Pro: Sharp Monocular Metric Depth in Less Than a Secondcited by 404
- 2024Panda-70M: Captioning 70M Videos with Multiple Cross-Modality Teacherscited by 393
- 2021Light Field Networks: Neural Scene Representations with Single-Evaluation Renderingcited by 332
- 2024Open-Sora Plan: Open-Source Large Video Generation Modelcited by 296
- 2020Infinite Nature: Perpetual View Generation of Natural Scenes from a Single Imagecited by 243
- 2023Visual Geometry Grounded Deep Structure From Motioncited by 227
- 2021Advancing High-Resolution Video-Language Representation with Large-Scale Video Transcriptionscited by 226
- 2024Motion-I2V: Consistent and Controllable Image-to-Video Generation with Explicit Motion Modelingcited by 211
- 2024WonderWorld: Interactive 3D Scene Generation from a Single Imagecited by 202
- 2025FLARE: Feed-forward Geometry, Appearance and Camera Estimation from Uncalibrated Sparse Viewscited by 180
- 2025Step-Video-T2V Technical Report: The Practice, Challenges, and Future of Video Foundation Modelcited by 168
- 2023WonderJourney: Going from Anywhere to Everywherecited by 158
- 2025GameFactory: Creating New Games with Generative Interactive Videoscited by 130
- 2024Snap Video: Scaled Spatiotemporal Transformers for Text-to-Video Synthesiscited by 105
- 2018Unsupervised Training for 3D Morphable Model Regressioncited by 100
- 2024Wonderland: Navigating 3D Scenes from a Single Imagecited by 99
- 2024SynCamMaster: Synchronizing Multi-Camera Video Generation from Diverse Viewpointscited by 93
- 2022InfiniteNature-Zero: Learning Perpetual View Generation of Natural Scenes from Single Imagescited by 82
- 2024The Matrix: Infinite-Horizon World Generation with Real-Time Moving Controlcited by 80
- 20243DTrajMaster: Mastering 3D Trajectory for Multi-Entity Motion in Video Generationcited by 71
- 2025RealCam-I2V: Real-World Image-to-Video Generation with Interactive Complex Camera Controlcited by 57
- 2022DiffDreamer: Towards Consistent Unsupervised Single-view Scene Extrapolation with Conditional Diffusion Modelscited by 51
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- 2024Generative Gaussian Splatting for Unbounded 3D City Generationcited by 44
- 2024I2VControl-Camera: Precise Video Camera Control with Adjustable Motion Strengthcited by 39
- 2025GS-DiT: Advancing Video Generation with Pseudo 4D Gaussian Fields through Efficient Dense 3D Point Trackingcited by 39
- 2022Treating Motion as Option to Reduce Motion Dependency in Unsupervised Video Object Segmentationcited by 35
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