mlsanityData to 14 Oct 2026

CoaG: Cylinders on a Grid for Coarse 3D Layout Control in Video Generation

arXiv 2609.24208 ↗First posted 21 Sep 2026cs.CVcited by 0

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Abstract (arXiv)

We ask how little geometry a person has to draw to control both where people stand and where the camera moves in a generated video. Our answer is a ground plane and one cylinder per person. A user draws a grid on the ground, places one cylinder where each person should stand, moves the cylinders and the camera over 81 frames, and the model renders a photoreal video in which the people occupy the cylinders' positions, move as the cylinders move, and are seen from the drawn camera. Appearance comes from a text prompt and a background reference image; layout and motion come from the geometry. Because no dataset pairs such a signal with video, we build the pairs ourselves: an automatic engine writes 2000 captions from a combinatorial seed, generates a clip for each with a text-to-video model, and lifts every clip back to its geometry with person tracking, background inpainting, an agentic ground-mask loop, feed-forward multi-view reconstruction and a plane fit, with no real footage and no manual labels. A LoRA on Wan2.2-Fun-Control trained on 1935 such tuples follows drawn layouts and camera paths on hold-out clips: the generated people match the cylinders' count, order, position and height, the text changes who they are, the reference image changes where they are, and dolly-in, orbit, pan and crane paths are followed, dolly-out only weakly.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 39 of its 53 references that are in the map. First posted on arXiv 21 Sep 2026.

Builds on

39 of 53 references.

References · 39

  1. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  2. 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,503
  3. 2025Wan: Open and Advanced Large-Scale Video Generative Modelscited by 2,708
  4. 2022InstructPix2Pix: Learning to Follow Image Editing Instructionscited by 2,611
  5. 2024HunyuanVideo: A Systematic Framework For Large Video Generative Modelscited by 1,660
  6. 2025VGGT: Visual Geometry Grounded Transformercited by 1,539
  7. 2025SAM 3: Segment Anything with Conceptscited by 906
  8. 2024WorldSimBench: Towards Video Generation Models as World Simulatorscited by 881
  9. 2023GLIGEN: Open-Set Grounded Text-to-Image Generationcited by 781
  10. 2021Resolution-robust Large Mask Inpainting with Fourier Convolutionscited by 738
  11. 2023Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animationcited by 688
  12. 2023MotionCtrl: A Unified and Flexible Motion Controller for Video Generationcited by 580
27 more
  1. 2023VideoComposer: Compositional Video Synthesis with Motion Controllabilitycited by 464
  2. 2022Kubric: A scalable dataset generatorcited by 381
  3. 2025GEN3C: 3D-Informed World-Consistent Video Generation with Precise Camera Controlcited by 341
  4. 2023MagicAnimate: Temporally Consistent Human Image Animation using Diffusion Modelcited by 330
  5. 2025ReCamMaster: Camera-Controlled Generative Rendering from A Single Videocited by 287
  6. 2024Champ: Controllable and Consistent Human Image Animation with 3D Parametric Guidancecited by 272
  7. 2023DragNUWA: Fine-grained Control in Video Generation by Integrating Text, Image, and Trajectorycited by 264
  8. 2025Diffusion as Shader: 3D-aware Video Diffusion for Versatile Video Generation Controlcited by 185
  9. 2024Motion Prompting: Controlling Video Generation with Motion Trajectoriescited by 178
  10. 2023SparseCtrl: Adding Sparse Controls to Text-to-Video Diffusion Modelscited by 175
  11. 2024Direct-a-Video: Customized Video Generation with User-Directed Camera Movement and Object Motioncited by 167
  12. 2024Tora: Trajectory-oriented Diffusion Transformer for Video Generationcited by 164
  13. 2024DragAnything: Motion Control for Anything using Entity Representationcited by 159
  14. 2024AC3D: Analyzing and Improving 3D Camera Control in Video Diffusion Transformerscited by 154
  15. 2025Go-with-the-Flow: Motion-Controllable Video Diffusion Models Using Real-Time Warped Noisecited by 117
  16. 2024Boximator: Generating Rich and Controllable Motions for Video Synthesiscited by 107
  17. 2025Uni3C: Unifying Precisely 3D-Enhanced Camera and Human Motion Controls for Video Generationcited by 77
  18. 2025CineMaster: A 3D-Aware and Controllable Framework for Cinematic Text-to-Video Generationcited by 74
  19. 20243DTrajMaster: Mastering 3D Trajectory for Multi-Entity Motion in Video Generationcited by 71
  20. 2023LooseControl: Lifting ControlNet for Generalized Depth Conditioningcited by 68
  21. 2024Ctrl-Adapter: An Efficient and Versatile Framework for Adapting Diverse Controls to Any Diffusion Modelcited by 66
  22. 2024GeoCalib: Learning Single-image Calibration with Geometric Optimizationcited by 52
  23. 2026VerseCrafter: Dynamic Realistic Video World Model with 4D Geometric Controlcited by 28
  24. 2025Perception-as-Control: Fine-grained Controllable Image Animation with 3D-aware Motion Representationcited by 23
  25. 2024CinePreGen: Camera Controllable Video Previsualization via Engine-powered Diffusioncited by 7
  26. 2026PrevizWhiz: Combining Rough 3D Scenes and 2D Video to Guide Generative Video Previsualizationcited by 4
  27. 2026LooseControlVideo: Directorial Video Control using Spatial Blockingcited by 2

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