# Cinematographic Camera Diffusion Model arXiv: https://arxiv.org/abs/2402.16143 ยท HTML: https://mlsanity.com/paper/2402.16143/ ## Key facts - Cited by 18 papers in the map (data through 14 Oct 2026). - Placed in Video generation, one of 14 directions in Diffusion models. - Builds on 23 of its 43 references that are in the map. - First posted on arXiv 25 Feb 2024. ## Placed in - Diffusion models / [Video generation](https://mlsanity.com/diffusion/video-generation/) ## Builds on (most cited first, 23 of 23) - [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021) - [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020) - [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021) - [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019) - [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021) - [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015) - [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022) - [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022) - [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022) - [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015) - [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013) - [DreamFusion: Text-to-3D using 2D Diffusion](https://mlsanity.com/paper/2209.14988/) (2022) - [Video Diffusion Models](https://mlsanity.com/paper/2204.03458/) (2022) - [RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2201.09865/) (2022) - [Human Motion Diffusion Model](https://mlsanity.com/paper/2209.14916/) (2022) - [Tackling the Generative Learning Trilemma with Denoising Diffusion GANs](https://mlsanity.com/paper/2112.07804/) (2021) - [GestureDiffuCLIP: Gesture Diffusion Model with CLIP Latents](https://mlsanity.com/paper/2303.14613/) (2023) - [A Local-to-Global Approach to Multi-modal Movie Scene Segmentation](https://mlsanity.com/paper/2004.02678/) (2020) - [The One Where They Reconstructed 3D Humans and Environments in TV Shows](https://mlsanity.com/paper/2207.14279/) (2022) - [Learning to Cut by Watching Movies](https://mlsanity.com/paper/2108.04294/) (2021) - [JAWS: Just A Wild Shot for Cinematic Transfer in Neural Radiance Fields](https://mlsanity.com/paper/2303.15427/) (2023) - [Directing Cinematographic Drones](https://mlsanity.com/paper/1712.04216/) (2017) - [Autonomous drone cinematographer: Using artistic principles to create smooth, safe, occlusion-free trajectories for aerial filming](https://mlsanity.com/paper/1808.09563/) (2018) ## Built on by (most cited first, 18 of 18) - [Towards Understanding Camera Motions in Any Video](https://mlsanity.com/paper/2504.15376/) (2025) - [Generative Photography: Scene-Consistent Camera Control for Realistic Text-to-Image Synthesis](https://mlsanity.com/paper/2412.02168/) (2024) - [E.T. the Exceptional Trajectories: Text-to-camera-trajectory generation with character awareness](https://mlsanity.com/paper/2407.01516/) (2024) - [AKiRa: Augmentation Kit on Rays for optical video generation](https://mlsanity.com/paper/2412.14158/) (2024) - [GenDoP: Auto-regressive Camera Trajectory Generation as a Director of Photography](https://mlsanity.com/paper/2504.07083/) (2025) - [Pulp Motion: Framing-aware multimodal camera and human motion generation](https://mlsanity.com/paper/2510.05097/) (2025) - [ShotVerse: Advancing Cinematic Camera Control for Text-Driven Multi-Shot Video Creation](https://mlsanity.com/paper/2603.11421/) (2026) - [DreamInsert: Zero-Shot Image-to-Video Object Insertion from A Single Image](https://mlsanity.com/paper/2503.10342/) (2025) - [AdaViewPlanner: Adapting Video Diffusion Models for Viewpoint Planning in 4D Scenes](https://mlsanity.com/paper/2510.10670/) (2025) - [LAMP: Language-Assisted Motion Planning for Controllable Video Generation](https://mlsanity.com/paper/2512.03619/) (2025) - [VERTIGO: Visual Preference Optimization for Cinematic Camera Trajectory Generation](https://mlsanity.com/paper/2604.02467/) (2026) - [ESA: Energy-Based Shot Assembly Optimization for Automatic Video Editing](https://mlsanity.com/paper/2511.02505/) (2025) - [Mind-of-Director: Multi-modal Agent-Driven Film Previsualization via Collaborative Decision-Making](https://mlsanity.com/paper/2603.14790/) (2026) - [Zero-Shot Personalized Camera Motion Control for Image-to-Video Synthesis](https://mlsanity.com/paper/2504.09472/) (2025) - [CineLOG: A Training Free Approach for Cinematic Long Video Generation](https://mlsanity.com/paper/2512.12209/) (2025) - [Auteur: Language-Driven Cinematographic Framing for Human-Centric Video Generation](https://mlsanity.com/paper/2606.01900/) (2026) - [AESOP: Asymmetric Human-Camera Generation with Translation-Intensity Control](https://mlsanity.com/paper/2609.37229/) (2026) - [Unveiling the Value of Motion for Cinematic Camera Trajectories](https://mlsanity.com/paper/2609.38683/) (2026) ## Abstract (arXiv) Designing effective camera trajectories in virtual 3D environments is a challenging task even for experienced animators. Despite an elaborate film grammar, forged through years of experience, that enables the specification of camera motions through cinematographic properties (framing, shots sizes, angles, motions), there are endless possibilities in deciding how to place and move cameras with characters. Dealing with these possibilities is part of the complexity of the problem. While numerous techniques have been proposed in the literature (optimization-based solving, encoding of empirical rules, learning from real examples,...), the results either lack variety or ease of control. In this paper, we propose a cinematographic camera diffusion model using a transformer-based architecture to handle temporality and exploit the stochasticity of diffusion models to generate diverse and qualitative trajectories conditioned by high-level textual descriptions. We extend the work by integrating keyframing constraints and the ability to blend naturally between motions using latent interpolation, in a way to augment the degree of control of the designers. We demonstrate the strengths of this text-to-camera motion approach through qualitative and quantitative experiments and gather feedback from professional artists. The code and data are available at URLhttps://github.com/jianghd1996/Camera-control.