# Auteur: Language-Driven Cinematographic Framing for Human-Centric Video Generation

arXiv: https://arxiv.org/abs/2606.01900 · HTML: https://mlsanity.com/paper/2606.01900/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 48 references that are in the map.
- First posted on arXiv 1 Jun 2026.

## Builds on (most cited first, 30 of 37)

- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024)
- [Wan: Open and Advanced Large-Scale Video Generative Models](https://mlsanity.com/paper/2503.20314/) (2025)
- [HunyuanVideo: A Systematic Framework For Large Video Generative Models](https://mlsanity.com/paper/2412.03603/) (2024)
- [Cosmos World Foundation Model Platform for Physical AI](https://mlsanity.com/paper/2501.03575/) (2025)
- [MotionCtrl: A Unified and Flexible Motion Controller for Video Generation](https://mlsanity.com/paper/2312.03641/) (2023)
- [VACE: All-in-One Video Creation and Editing](https://mlsanity.com/paper/2503.07598/) (2025)
- [GEN3C: 3D-Informed World-Consistent Video Generation with Precise Camera Control](https://mlsanity.com/paper/2503.03751/) (2025)
- [ReCamMaster: Camera-Controlled Generative Rendering from A Single Video](https://mlsanity.com/paper/2503.11647/) (2025)
- [VideoDirectorGPT: Consistent Multi-scene Video Generation via LLM-Guided Planning](https://mlsanity.com/paper/2309.15091/) (2023)
- [AuroraCap: Efficient, Performant Video Detailed Captioning and a New Benchmark](https://mlsanity.com/paper/2410.03051/) (2024)
- [TrajectoryCrafter: Redirecting Camera Trajectory for Monocular Videos via Diffusion Models](https://mlsanity.com/paper/2503.05638/) (2025)
- [Training-free Camera Control for Video Generation](https://mlsanity.com/paper/2406.10126/) (2024)
- [TRAM: Global Trajectory and Motion of 3D Humans from in-the-wild Videos](https://mlsanity.com/paper/2403.17346/) (2024)
- [CameraCtrl II: Dynamic Scene Exploration via Camera-controlled Video Diffusion Models](https://mlsanity.com/paper/2503.10592/) (2025)
- [Condensed Movies: Story Based Retrieval with Contextual Embeddings](https://mlsanity.com/paper/2005.04208/) (2020)
- [Uni3C: Unifying Precisely 3D-Enhanced Camera and Human Motion Controls for Video Generation](https://mlsanity.com/paper/2504.14899/) (2025)
- [CineMaster: A 3D-Aware and Controllable Framework for Cinematic Text-to-Video Generation](https://mlsanity.com/paper/2502.08639/) (2025)
- [HumanVid: Demystifying Training Data for Camera-controllable Human Image Animation](https://mlsanity.com/paper/2407.17438/) (2024)
- [RealCam-I2V: Real-World Image-to-Video Generation with Interactive Complex Camera Control](https://mlsanity.com/paper/2502.10059/) (2025)
- [Director3D: Real-world Camera Trajectory and 3D Scene Generation from Text](https://mlsanity.com/paper/2406.17601/) (2024)
- [FloVD: Optical Flow Meets Video Diffusion Model for Enhanced Camera-Controlled Video Synthesis](https://mlsanity.com/paper/2502.08244/) (2025)
- [VerseCrafter: Dynamic Realistic Video World Model with 4D Geometric Control](https://mlsanity.com/paper/2601.05138/) (2026)
- [E.T. the Exceptional Trajectories: Text-to-camera-trajectory generation with character awareness](https://mlsanity.com/paper/2407.01516/) (2024)
- [VidCRAFT3: Camera, Object, and Lighting Control for Image-to-Video Generation](https://mlsanity.com/paper/2502.07531/) (2025)
- [EPiC: Efficient Video Camera Control Learning with Precise Anchor-Video Guidance](https://mlsanity.com/paper/2505.21876/) (2025)
- [Boosting Camera Motion Control for Video Diffusion Transformers](https://mlsanity.com/paper/2410.10802/) (2024)
- [I2VControl: Disentangled and Unified Video Motion Synthesis Control](https://mlsanity.com/paper/2411.17765/) (2024)
- [Cinematographic Camera Diffusion Model](https://mlsanity.com/paper/2402.16143/) (2024)
- [GenDoP: Auto-regressive Camera Trajectory Generation as a Director of Photography](https://mlsanity.com/paper/2504.07083/) (2025)
- [Vista4D: Video Reshooting with 4D Point Clouds](https://mlsanity.com/paper/2604.21915/) (2026)

## Built on by (most cited first, 1 of 1)

- [AESOP: Asymmetric Human-Camera Generation with Translation-Intensity Control](https://mlsanity.com/paper/2609.37229/) (2026)

## Abstract (arXiv)

Generative video models have achieved remarkable visual fidelity and temporal coherence, yet intentional camera control remains elusive. Existing frameworks treat camera motion as a byproduct of pixel synthesis, producing trajectories that are stochastic, spatially inconsistent, and indifferent to the human subject driving the scene. In this work, we present Auteur, a method for language-driven, human-centric camera framing in generative video. Our core insight is that professional filmmakers conceive shots not as world-space trajectories but as framings defined relative to the actor, encoding shot size, angle, and composition as functions of human pose and motion. We formalize this intuition as a human-centric camera parameterization and introduce a Domain-Specific Language (DSL) that is convertible to standard 6-DoF camera parameters. A fine-tuned multimodal large language model then acts as a virtual director, mapping natural language descriptions and coarse human motion to sparse DSL keyframes that are deterministically interpolated into continuous camera trajectories, which are then provided as input to video generators. We train and evaluate Auteur on a new dataset of 34K aligned text, human motion, and DSL-annotated camera trajectories drawn from procedural synthesis and real-world movie footage from the CondensedMovies dataset. Auteur enables cinematographic framing of human-centered scenes, a capability largely absent in prior generative models. To assess this behavior, we propose new framing-focused metrics, and our experiments show that Auteur consistently outperforms existing methods. Project page is https://cyberiada.github.io/Auteur/