DreamCinema: Cinematic Transfer with Free Camera and 3D Character
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Abstract (arXiv)
We are living in a flourishing era of digital media, where everyone has the potential to become a personal filmmaker. Current research on video generation suggests a promising avenue for controllable film creation in pixel space using Diffusion models. However, the reliance on overly verbose prompts and insufficient focus on cinematic elements (e.g., camera movement) results in videos that lack cinematic quality. Furthermore, the absence of 3D modeling often leads to failures in video generation, such as inconsistent character models at different frames, ultimately hindering the immersive experience for viewers. In this paper, we propose a new framework for film creation, Dream-Cinema, which is designed for user-friendly, 3D space-based film creation with generative models. Specifically, we decompose 3D film creation into four key elements: 3D character, driven motion, camera movement, and environment. We extract the latter three elements from user-specified film shots and generate the 3D character using a generative model based on a provided image. To seamlessly recombine these elements and ensure smooth film creation, we propose structure-guided character animation, shape-aware camera movement optimization, and environment-aware generative refinement. Extensive experiments demonstrate the effectiveness of our method in generating high-quality films with free camera and 3D characters.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 50 of its 78 references that are in the map. First posted on arXiv 22 Aug 2024.
Builds on
50 of 78 references.
References · 50
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
- 2020Denoising Diffusion Probabilistic Modelscited by 21,076
- 2020Denoising Diffusion Implicit Modelscited by 9,781
- 2023Segment Anythingcited by 8,234
- 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,503
- 2022DreamFusion: Text-to-3D using 2D Diffusioncited by 3,025
- 2023Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasetscited by 2,581
- 2021SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equationscited by 1,937
- 2023VBench: Comprehensive Benchmark Suite for Video Generative Modelscited by 1,822
- 2019Expressive Body Capture: 3D Hands, Face, and Body from a Single Imagecited by 1,612
- 2023Zero-1-to-3: Zero-shot One Image to 3D Objectcited by 1,489
- 2020D-NeRF: Neural Radiance Fields for Dynamic Scenescited by 1,465
38 more
- 2022Magic3D: High-Resolution Text-to-3D Content Creationcited by 1,290
- 2021MUSIQ: Multi-scale Image Quality Transformercited by 1,081
- 2016Realtime Multi-Person 2D Pose Estimation using Part Affinity Fieldscited by 1,064
- 2022Diffusion Models in Vision: A Surveycited by 1,015
- 2016Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Imagecited by 695
- 2023Wonder3D: Single Image to 3D using Cross-Domain Diffusioncited by 689
- 2023Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animationcited by 688
- 2021DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Camerascited by 687
- 2023Fantasia3D: Disentangling Geometry and Appearance for High-quality Text-to-3D Content Creationcited by 687
- 2021NICE-SLAM: Neural Implicit Scalable Encoding for SLAMcited by 589
- 2024Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Modelscited by 477
- 2020INeRF: Inverting Neural Radiance Fields for Pose Estimationcited by 382
- 2023MagicAnimate: Temporally Consistent Human Image Animation using Diffusion Modelcited by 330
- 2021HuMoR: 3D Human Motion Model for Robust Pose Estimationcited by 324
- 2023Triplane Meets Gaussian Splatting: Fast and Generalizable Single-View 3D Reconstruction with Transformerscited by 258
- 2024StreamingT2V: Consistent, Dynamic, and Extendable Long Video Generation from Textcited by 251
- 2023SEINE: Short-to-Long Video Diffusion Model for Generative Transition and Predictioncited by 233
- 2023A Survey on Video Diffusion Modelscited by 206
- 2023WHAM: Reconstructing World-grounded Humans with Accurate 3D Motioncited by 206
- 2017Unite the People: Closing the Loop Between 3D and 2D Human Representationscited by 205
- 2023ProPainter: Improving Propagation and Transformer for Video Inpaintingcited by 197
- 2023DreamPose: Fashion Image-to-Video Synthesis via Stable Diffusioncited by 167
- 2023Decoupling Human and Camera Motion from Videos in the Wildcited by 142
- 2023DisCo: Disentangled Control for Realistic Human Dance Generationcited by 142
- 2024Unique3D: High-Quality and Efficient 3D Mesh Generation from a Single Imagecited by 135
- 2023VideoBooth: Diffusion-based Video Generation with Image Promptscited by 126
- 2024UniAnimate: Taming Unified Video Diffusion Models for Consistent Human Image Animationcited by 102
- 2024Sora as a World Model? A Complete Survey on Text-to-Video Generationcited by 53
- 2020Deep Spatial Transformation for Pose-Guided Person Image Generation and Animationcited by 43
- 2024A Survey on Generative AI and LLM for Video Generation, Understanding, and Streamingcited by 37
- 2023Sherpa3D: Boosting High-Fidelity Text-to-3D Generation via Coarse 3D Priorcited by 32
- 2024Make-Your-3D: Fast and Consistent Subject-Driven 3D Content Generationcited by 32
- 2024From Sora What We Can See: A Survey of Text-to-Video Generationcited by 27
- 2023JAWS: Just A Wild Shot for Cinematic Transfer in Neural Radiance Fieldscited by 13
- 2022Few-shot Image Generation via Masked Discriminationcited by 12
- 2023Cinematic Behavior Transfer via NeRF-based Differentiable Filmingcited by 12
- 2022Single-view 3D Mesh Reconstruction for Seen and Unseen Categoriescited by 10
- 2022Self-supervised Matting-specific Portrait Enhancement and Generationcited by 3
Built on by
Citing papers per year: 2024: 2; 2025: 6; 2026: 1.
9 papers.
- 20243DTrajMaster: Mastering 3D Trajectory for Multi-Entity Motion in Video Generationcited by 71
- 2024AKiRa: Augmentation Kit on Rays for optical video generationcited by 18
- 2025GenWorld: Towards Detecting AI-generated Real-world Simulation Videoscited by 8
- 2025Pulp Motion: Framing-aware multimodal camera and human motion generationcited by 8
- 20253D Object Manipulation in a Single Image using Generative Modelscited by 7
- 2025GenHSI: Controllable Generation of Human-Scene Interaction Videoscited by 6
- 2025SceneCompleter: Dense 3D Scene Completion for Generative Novel View Synthesiscited by 5
- 2025Enhanced Velocity Field Modeling for Gaussian Video Reconstructioncited by 0
- 2026UniMo: Unifying Human and Animal Motion Generationcited by 0
- 2026UniMo: Unifying Human and Animal Motion Generation11 Sep 2026
- 2025Pulp Motion: Framing-aware multimodal camera and human motion generation6 Oct 2025
- 2025Enhanced Velocity Field Modeling for Gaussian Video Reconstruction31 Jul 2025
- 2025GenHSI: Controllable Generation of Human-Scene Interaction Videos24 Jun 2025
- 2025SceneCompleter: Dense 3D Scene Completion for Generative Novel View Synthesis12 Jun 2025
- 2025GenWorld: Towards Detecting AI-generated Real-world Simulation Videos12 Jun 2025
- 20253D Object Manipulation in a Single Image using Generative Models22 Jan 2025
- 2024AKiRa: Augmentation Kit on Rays for optical video generation18 Dec 2024
- 20243DTrajMaster: Mastering 3D Trajectory for Multi-Entity Motion in Video Generation10 Dec 2024
Similar, not cited
Close in wording, no citation either way
- 2025MotionCanvas: Cinematic Shot Design with Controllable Image-to-Video Generation
- 2026DreamLoop: Controllable Cinemagraph Generation from a Single Photograph
- 2026Rotate Your Character: Revisiting Video Diffusion Models for High-Quality 3D Character Generation
- 2026DreamShot: Personalized Storyboard Synthesis with Video Diffusion Prior
- 2025DreamJourney: Perpetual View Generation with Video Diffusion Models
- 2024Cinematographic Camera Diffusion Model
- 2025DreaMontage: Arbitrary Frame-Guided One-Shot Video Generation
- 20253D Scene Generation: A Survey