CameraAnything: Refilming Videos with Arbitrary Camera Control
CameraAnything integrates pixel-level Plücker rays and resolution-aware 3D RoPE in a diffusion framework for joint camera parameter control, supporting viewpoint, focal length, and resolution adjustments.
Key Findings
Methodology
The approach builds on a diffusion model combined with Plücker ray encoding and resolution-aware 3D RoPE within self-attention. It synthesizes multi-camera scene data via a synthetic pipeline, employing an orthogonal training strategy to enable simultaneous control over camera position, focal length, and resolution without cropping or outpainting. Key components include: • Pixel-wise Plücker ray encoding capturing spatial and geometric relationships; • Integration of RoPE into transformer layers to enhance spatial positional encoding; • Construction of diverse multi-camera synthetic datasets for multi-task learning.
Key Results
- The model outperforms baselines with PSNR improvements of about 3.5dB, SSIM reaching 0.92, and LPIPS dropping to 0.12, demonstrating high-fidelity reconstructions.
- It achieves smooth, continuous viewpoint and focal length transitions, supporting up to 12 parameter combinations, and maintains detail consistency across resolution changes.
- Experiments on synthetic and real videos show strong generalization, enabling high-quality re-shoots across different display formats and platforms.
Significance
This work addresses the longstanding challenge of unified multi-parameter camera control in video editing, enabling expressive storytelling and content adaptation. It bridges the gap between static extrinsic pose adjustments and intrinsic parameter tuning, opening new possibilities for cinematic production, virtual reality, and cross-platform content delivery. The synthetic data pipeline further reduces reliance on large annotated datasets, making advanced camera control more accessible.
Technical Contribution
Innovations include: • Pixel-level Plücker ray encoding for detailed geometric representation; • Resolution-aware 3D RoPE for multi-scale parameter integration; • Synthetic multi-camera data generation with orthogonal training for multi-task learning. These enable a unified, flexible framework surpassing prior methods limited to single-parameter or static scene control.
Novelty
This is the first unified framework supporting joint control of camera position, focal length, and resolution within a single process, combining pixel-level geometric encoding with resolution-aware positional embedding. It significantly extends prior work that only handled extrinsic pose or limited intrinsic parameters, offering a comprehensive solution for expressive video re-shooting.
Limitations
- Performance drops in highly dynamic scenes with complex occlusions, mainly due to synthetic data limitations and scene complexity.
- Domain gap between synthetic training data and real-world videos may affect real-world applicability, requiring further domain adaptation.
- Inference speed remains relatively slow, limiting real-time applications; optimization is needed for practical deployment.
Future Work
Future directions include incorporating real-world datasets to improve robustness, optimizing model architecture for faster inference, and expanding control dimensions to include lighting, color grading, and other visual effects. Additionally, integrating user-guided editing interfaces could enhance usability for content creators.
AI Executive Summary
CameraAnything introduces a groundbreaking unified framework for camera-controlled video editing, capable of joint manipulation of viewpoint, focal length, and resolution. Building upon a diffusion model foundation, it employs pixel-level Plücker ray encoding and resolution-aware 3D RoPE to embed detailed geometric and spatial information directly into the transformer’s self-attention layers. This design allows the model to understand and manipulate the complex interplay between intrinsic and extrinsic camera parameters seamlessly.
The core innovation lies in synthesizing diverse multi-camera datasets through a structured pipeline, enabling the model to learn multi-task control strategies. During training, parameters such as camera pose, focal length, and resolution are sampled orthogonally, allowing the model to perform both single-parameter adjustments and complex multi-parameter transformations. Experimental results demonstrate that the model surpasses existing methods in fidelity, stability, and flexibility, supporting multi-shot transitions, viewpoint changes, and zoom effects with high consistency.
This work has significant implications for cinematic production, virtual reality, and cross-platform content adaptation. It democratizes advanced camera control, reducing reliance on expensive equipment and manual editing. While promising, challenges remain in handling highly dynamic scenes, reducing inference latency, and bridging the synthetic-real domain gap. Future research will focus on real-world data integration, speed optimization, and broader control capabilities, aiming to make expressive, high-quality video re-shooting accessible to all creators.
Deep Analysis
Background
随着深度学习和生成模型的快速发展，视频编辑逐渐走向智能化。早期方法多依赖3D重建或手工调节，代表技术包括Neural Radiance Fields (NeRF)和GAN-based编辑。近年来，扩散模型如Stable Diffusion在图像生成中表现出色，推动了视频生成技术的革新，但多参数摄像机控制仍面临几何一致性和数据不足的挑战。现有方法多支持外参调控，缺乏对焦距和分辨率的联合调节，限制了表达力和应用场景。
Core Problem
核心问题在于如何在动态场景中同时调控摄像机的内外参数，尤其是在保持几何一致性和细节完整性方面。传统方法依赖昂贵的3D重建或有限参数调节，难以实现高效、连续、多参数的联合控制。缺乏多样化训练数据也限制了模型的泛化能力，尤其在真实场景中的应用受到阻碍。
Innovation
本研究的创新点包括：1）像素级Plücker光线编码，细粒度描述空间几何关系；2）结合resolution-aware 3D RoPE，支持多尺度、多参数调控；3）构建多摄像机合成数据管线，支持多任务正交训练。这些设计使模型能在单一流程中实现视角、焦距、分辨率的联合调控，突破了只支持单一参数或静态场景的限制。
Methodology
- �� 构建扩散模型基础架构，结合3D VAE编码视频；• 利用Plücker光线编码像素空间方向和位置，捕获几何信息；• 在Transformer中注入RoPE，增强空间位置编码的几何一致性；• 设计多摄像机合成数据管线，生成多样化训练样本；• 采用正交训练策略，随机调控外参、焦距和分辨率，支持多任务学习；• 通过多任务训练，实现多参数联合调控能力。
Experiments
采用合成数据集和真实视频进行评估，数据由Unreal Engine 5生成，涵盖多场景、多参数变化。指标包括PSNR、SSIM、LPIPS、FVD等，比较基线有TrajectoryCrafter和ReCamMaster。模型在多参数调控任务中表现优异，支持多达12种参数组合，且在不同分辨率下保持细节。消融实验验证了Plücker光线和RoPE的有效性，展现出良好的泛化能力。
Results
模型在PSNR提升3.5dB，SSIM达0.92，LPIPS降至0.12，FVD优于对比方法。支持多镜头切换和焦距调节，能实现连续平滑的视角变换。在不同平台和显示比例下，保持高质量重拍效果，验证其广泛适用性。
Applications
适用于电影后期、虚拟现实内容生成、跨平台内容适配等。用户只需提供输入视频和目标参数，模型即可自动生成符合需求的多视角、多参数视频，极大降低专业门槛。
Limitations & Outlook
在极端动态场景和遮挡条件下表现仍有限，合成数据偏差影响真实场景适应性，推理速度需优化以满足实时需求。未来将增强模型鲁棒性和效率。
Plain Language Accessible to non-experts
想象你在拍电影，导演希望用不同角度、焦距和画面大小来表现场景。传统方法需要很多设备和反复拍摄，而这个新技术像有个聪明的助手，能在电脑里模拟各种拍摄效果。你只要告诉它想看哪个角度、远一点还是近一点，它就能用计算机“想象”出这些镜头，然后自动生成视频。它通过理解每个像素在空间中的位置和相机的运动，像在脑海中“设计”不同的镜头，然后用电脑把这些设计变成真实的视频。这样，不管你想换个视角、拉远或拉近，甚至改变画面大小，都能轻松实现，就像魔法一样。这让电影制作变得更简单、更灵活，也为虚拟现实和内容创作带来无限可能。
ELI14 Explained like you're 14
想象你在玩一个超级酷的游戏，你可以随意切换视角，像变魔术一样看到不同的角度。以前，要做到这一点需要很多设备和专业知识，但现在有了这个新技术，就像有个聪明的机器人帮你做这些事情。你只要告诉它你想看哪个角度、远一点还是近一点，它就能用电脑模拟出这些效果。它通过理解每个像素在空间中的位置和相机的运动轨迹，像在脑海中“想象”不同的镜头，然后用计算机把这些“想象”变成真实的视频。这样，你可以随意换视角、调整焦距和画面大小，就像在玩变形金刚一样酷！这让电影、游戏和虚拟现实变得更有趣、更方便，也让普通人也能创造出专业水平的内容。
Glossary
Plücker光线 (Plücker Ray)
一种描述空间中射线的数学表示，结合射线的方向和位置，用于捕获相机几何信息。论文中用于像素级几何编码。
在模型中用来编码每个像素的空间方向和相机位置，支持多参数调控。
3D RoPE (3D Rotary Positional Encoding)
一种在Transformer中引入空间位置关系的编码机制，通过旋转编码增强多尺度空间信息的表达能力。论文中用于几何一致性增强。
结合Plücker光线，注入Transformer的自注意力层，支持多参数联合调控。
扩散模型 (Diffusion Model)
一种生成模型，通过逐步去噪实现高质量图像或视频合成，具有强大的生成能力。论文中用于视频生成。
基础架构为Wan2.1-T2V-1.3B，支持视频的条件生成和重建。
Open Questions Unanswered questions from this research
- 1 如何进一步提升模型在极端动态场景下的几何一致性，特别是遮挡和快速运动中的表现？
- 2 在真实场景中，如何解决合成数据与实际数据的域差异问题？
- 3 模型推理速度仍需优化以满足实时视频编辑需求。
Applications
Immediate Applications
电影虚拟摄像
利用模型实现多视角、多焦距的虚拟拍摄，降低成本，提高后期效率。
虚拟现实内容生成
快速生成多角度虚拟场景，丰富VR体验，支持交互式内容定制。
Long-term Vision
跨平台内容自适应
实现内容在不同设备和屏幕比例下的自动优化，推动多屏互动和个性化内容分发。
Abstract
We introduce CameraAnything, the first unified framework for camera controlled video editing that enables joint control of both intrinsic and extrinsic camera parameters. Existing approaches either rely on expensive 3D reconstruction to achieve full camera functionality or restrict editing to extrinsic parameter manipulation. Moreover, the coupled influence of intrinsic and extrinsic parameters on video appearance makes disentangled modeling particularly challenging. To address this, we adopt per-pixel Plücker ray injection alongside resolution-aware 3D RoPE in self-attention, building both camera conditioning and spatial positional encoding on the target latent to jointly control camera position, focal length, and native resolution editing without cropping or outpainting. To overcome the scarcity of paired training data, we further develop a scalable synthetic pipeline that constructs diverse dynamic scenes through structured multi-camera recording and generates synchronized videos with varied camera configurations. With a tailored orthogonal training strategy, CameraAnything enables expressive video reshooting with arbitrary viewpoint control, focal length adjustment, resolution adaptation, and multi-shot transitions within a single generation process, offering strong practical value for cinematic video editing and cross-platform content adaptation in video production.