# Boosting Camera Motion Control for Video Diffusion Transformers

arXiv: https://arxiv.org/abs/2410.10802 · HTML: https://mlsanity.com/paper/2410.10802/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Placed in Video generation, one of 14 directions in Diffusion models.
- Builds on 30 of its 39 references that are in the map.
- First posted on arXiv 14 Oct 2024.

## Placed in

- Diffusion models / [Video generation](https://mlsanity.com/diffusion/video-generation/)

## Builds on (most cited first, 30 of 30)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets](https://mlsanity.com/paper/2311.15127/) (2023)
- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [Video Diffusion Models](https://mlsanity.com/paper/2204.03458/) (2022)
- [CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer](https://mlsanity.com/paper/2408.06072/) (2024)
- [VBench: Comprehensive Benchmark Suite for Video Generative Models](https://mlsanity.com/paper/2311.17982/) (2023)
- [AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning](https://mlsanity.com/paper/2307.04725/) (2023)
- [MotionCtrl: A Unified and Flexible Motion Controller for Video Generation](https://mlsanity.com/paper/2312.03641/) (2023)
- [Latte: Latent Diffusion Transformer for Video Generation](https://mlsanity.com/paper/2401.03048/) (2024)
- [Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models](https://mlsanity.com/paper/2402.17177/) (2024)
- [CameraCtrl: Enabling Camera Control for Text-to-Video Generation](https://mlsanity.com/paper/2404.02101/) (2024)
- [Pose Guided Person Image Generation](https://mlsanity.com/paper/1705.09368/) (2017)
- [SparseCtrl: Adding Sparse Controls to Text-to-Video Diffusion Models](https://mlsanity.com/paper/2311.16933/) (2023)
- [Direct-a-Video: Customized Video Generation with User-Directed Camera Movement and Object Motion](https://mlsanity.com/paper/2402.03162/) (2024)
- [CamCo: Camera-Controllable 3D-Consistent Image-to-Video Generation](https://mlsanity.com/paper/2406.02509/) (2024)
- [Tora: Trajectory-oriented Diffusion Transformer for Video Generation](https://mlsanity.com/paper/2407.21705/) (2024)
- [VD3D: Taming Large Video Diffusion Transformers for 3D Camera Control](https://mlsanity.com/paper/2407.12781/) (2024)
- [Stability](https://mlsanity.com/paper/1310.0150/) (2013)
- [HumanSD: A Native Skeleton-Guided Diffusion Model for Human Image Generation](https://mlsanity.com/paper/2304.04269/) (2023)
- [Neural Texture Extraction and Distribution for Controllable Person Image Synthesis](https://mlsanity.com/paper/2204.06160/) (2022)
- [KPE: Keypoint Pose Encoding for Transformer-based Image Generation](https://mlsanity.com/paper/2203.04907/) (2022)
- [UPGPT: Universal Diffusion Model for Person Image Generation, Editing and Pose Transfer](https://mlsanity.com/paper/2304.08870/) (2023)
- [ViscoNet: Bridging and Harmonizing Visual and Textual Conditioning for ControlNet](https://mlsanity.com/paper/2312.03154/) (2023)

## Built on by (most cited first, 20 of 20)

- [Identity-Preserving Text-to-Video Generation by Frequency Decomposition](https://mlsanity.com/paper/2411.17440/) (2024)
- [AC3D: Analyzing and Improving 3D Camera Control in Video Diffusion Transformers](https://mlsanity.com/paper/2411.18673/) (2024)
- [Wonderland: Navigating 3D Scenes from a Single Image](https://mlsanity.com/paper/2412.12091/) (2024)
- [Towards Understanding Camera Motions in Any Video](https://mlsanity.com/paper/2504.15376/) (2025)
- [Lyra: Generative 3D Scene Reconstruction via Video Diffusion Model Self-Distillation](https://mlsanity.com/paper/2509.19296/) (2025)
- [Unified Camera Positional Encoding for Controlled Video Generation](https://mlsanity.com/paper/2512.07237/) (2025)
- [Reangle-A-Video: 4D Video Generation as Video-to-Video Translation](https://mlsanity.com/paper/2503.09151/) (2025)
- [Generative Photography: Scene-Consistent Camera Control for Realistic Text-to-Image Synthesis](https://mlsanity.com/paper/2412.02168/) (2024)
- [Geometry-aware 4D Video Generation for Robot Manipulation](https://mlsanity.com/paper/2507.01099/) (2025)
- [AKiRa: Augmentation Kit on Rays for optical video generation](https://mlsanity.com/paper/2412.14158/) (2024)
- [Ingredients: Blending Custom Photos with Video Diffusion Transformers](https://mlsanity.com/paper/2501.01790/) (2025)
- [ConMo: Controllable Motion Disentanglement and Recomposition for Zero-Shot Motion Transfer](https://mlsanity.com/paper/2504.02451/) (2025)
- [TokenMotion: Decoupled Motion Control via Token Disentanglement for Human-centric Video Generation](https://mlsanity.com/paper/2504.08181/) (2025)
- [Pulp Motion: Framing-aware multimodal camera and human motion generation](https://mlsanity.com/paper/2510.05097/) (2025)
- [DropletVideo: A Dataset and Approach to Explore Integral Spatio-Temporal Consistent Video Generation](https://mlsanity.com/paper/2503.06053/) (2025)
- [TeRA: Rethinking Text-guided Realistic 3D Avatar Generation](https://mlsanity.com/paper/2509.02466/) (2025)
- [Track2View: 4D-Consistent Camera-Controlled Video Generation via Paired 3D Point Tracks](https://mlsanity.com/paper/2606.15534/) (2026)
- [Auteur: Language-Driven Cinematographic Framing for Human-Centric Video Generation](https://mlsanity.com/paper/2606.01900/) (2026)
- [Steady-Forcing: Balancing Spatial Persistence and Motion Continuity in Long-Horizon Nature Video Diffusion](https://mlsanity.com/paper/2606.14732/) (2026)
- [VividCam: Learning Unconventional Camera Motions from Virtual Synthetic Videos](https://mlsanity.com/paper/2510.24904/) (2025)

## Abstract (arXiv)

Recent advancements in diffusion models have significantly enhanced the quality of video generation. However, fine-grained control over camera pose remains a challenge. While U-Net-based models have shown promising results for camera control, transformer-based diffusion models (DiT)-the preferred architecture for large-scale video generation - suffer from severe degradation in camera motion accuracy. In this paper, we investigate the underlying causes of this issue and propose solutions tailored to DiT architectures. Our study reveals that camera control performance depends heavily on the choice of conditioning methods rather than camera pose representations that is commonly believed. To address the persistent motion degradation in DiT, we introduce Camera Motion Guidance (CMG), based on classifier-free guidance, which boosts camera control by over 400%. Additionally, we present a sparse camera control pipeline, significantly simplifying the process of specifying camera poses for long videos. Our method universally applies to both U-Net and DiT models, offering improved camera control for video generation tasks.