CameraCtrl: Enabling Camera Control for Text-to-Video Generation
On the map: Diffusion models → Video generation
Abstract (arXiv)
Controllability plays a crucial role in video generation, as it allows users to create and edit content more precisely. Existing models, however, lack control of camera pose that serves as a cinematic language to express deeper narrative nuances. To alleviate this issue, we introduce CameraCtrl, enabling accurate camera pose control for video diffusion models. Our approach explores effective camera trajectory parameterization along with a plug-and-play camera pose control module that is trained on top of a video diffusion model, leaving other modules of the base model untouched. Moreover, a comprehensive study on the effect of various training datasets is conducted, suggesting that videos with diverse camera distributions and similar appearance to the base model indeed enhance controllability and generalization. Experimental results demonstrate the effectiveness of CameraCtrl in achieving precise camera control with different video generation models, marking a step forward in the pursuit of dynamic and customized video storytelling from textual and camera pose inputs.
Key facts. Cited by 448 papers in the map (data through 7 Oct 2026). Placed in Video generation, one of 14 directions in Diffusion models. Builds on 53 of its 72 references that are in the map. First posted on arXiv 2 Apr 2024.
Builds on
53 of 72 references.
Closest ancestors · 18
Same direction, newest first
- 2024Direct-a-Video: Customized Video Generation with User-Directed Camera Movement and Object Motion5 Feb
- 2024Lumiere: A Space-Time Diffusion Model for Video Generation23 Jan
- 2024Latte: Latent Diffusion Transformer for Video Generation5 Jan
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- 2023Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation28 Nov
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- 2023Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets25 Nov
- 2023I2VGen-XL: High-Quality Image-to-Video Synthesis via Cascaded Diffusion Models7 Nov
- 2023SEINE: Short-to-Long Video Diffusion Model for Generative Transition and Prediction31 Oct
- 2023AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning10 Jul
- 2023ControlVideo: Training-free Controllable Text-to-Video Generation22 May
6 more
- 2023Motion-Conditioned Diffusion Model for Controllable Video Synthesis27 Apr
- 2023Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models18 Apr
- 2023Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generators23 Mar
- 2023Structure and Content-Guided Video Synthesis with Diffusion Models6 Feb
- 2022Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation22 Dec
- 2022Imagen Video: High Definition Video Generation with Diffusion Models5 Oct
Other references · 35
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
- 2020Denoising Diffusion Probabilistic Modelscited by 20,851
- 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
- 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
- 2020Denoising Diffusion Implicit Modelscited by 9,655
- 2022Scalable Diffusion Models with Transformerscited by 5,931
- 2022Classifier-Free Diffusion Guidancecited by 5,717
- 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,466
- 2023SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesiscited by 4,290
- 2022DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generationcited by 3,286
- 2022Elucidating the Design Space of Diffusion-Based Generative Modelscited by 2,994
- 2020RAFT: Recurrent All-Pairs Field Transforms for Optical Flowcited by 2,385
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- 2022Make-A-Video: Text-to-Video Generation without Text-Video Datacited by 1,725
- 2022Objaverse: A Universe of Annotated 3D Objectscited by 1,522
- 2023IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Modelscited by 1,439
- 2023T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Modelscited by 1,339
- 2018Towards Accurate Generative Models of Video: A New Metric & Challengescited by 1,204
- 2021Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrievalcited by 1,175
- 2022CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformerscited by 973
- 2024Grounded SAM: Assembling Open-World Models for Diverse Visual Taskscited by 914
- 2023MotionCtrl: A Unified and Flexible Motion Controller for Video Generationcited by 568
- 2023VideoPoet: A Large Language Model for Zero-Shot Video Generationcited by 479
- 2023VideoComposer: Compositional Video Synthesis with Motion Controllabilitycited by 461
- 2021Light Field Networks: Neural Scene Representations with Single-Evaluation Renderingcited by 330
- 2023Follow Your Pose: Pose-Guided Text-to-Video Generation using Pose-Free Videoscited by 312
- 2023DragNUWA: Fine-grained Control in Video Generation by Integrating Text, Image, and Trajectorycited by 260
- 2020Infinite Nature: Perpetual View Generation of Natural Scenes from a Single Imagecited by 239
- 2022MM-Diffusion: Learning Multi-Modal Diffusion Models for Joint Audio and Video Generationcited by 225
- 2023MVImgNet: A Large-scale Dataset of Multi-view Imagescited by 219
- 2024Motion-I2V: Consistent and Controllable Image-to-Video Generation with Explicit Motion Modelingcited by 209
- 2023Make-Your-Video: Customized Video Generation Using Textual and Structural Guidancecited by 129
- 2019Learning the Depths of Moving People by Watching Frozen Peoplecited by 120
- 2024Snap Video: Scaled Spatiotemporal Transformers for Text-to-Video Synthesiscited by 105
- 2023Compositional 3D Scene Generation using Locally Conditioned Diffusioncited by 101
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Citing papers per year: 2024: 64; 2025: 198; 2026: 186.
448 papers.
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