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Gen Through the Lens: Camera-Controllable Video Generation via Cross-Coordinate Conditioning Fusion

arXiv 2610.15900 ↗First posted 11 Oct 2026cs.CV cs.AI cs.GRcited by 0

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Abstract (arXiv)

We present Gen Through the Lens, a camera-controllable image-to-video generation framework that synthesizes videos from a given reference image while enabling realistic and physically consistent camera operations. Unlike previous models that treat camera motion as abstract conditioning or rely on a single coordinate system, our approach explicitly decomposes and fuses control signals across three coordinate spaces: the camera coordinate (modeled by intrinsic parameters), the world coordinate (represented by spatial geometry hints), and the object coordinate (described by 3D bounding boxes of target subjects). Through cross-coordinate conditioning fusion, these heterogeneous cues are unified to guide the generative process, enabling precise control of camera motion, focus, and framing while preserving temporal and spatial coherence. Extensive experiments demonstrate that our method produces controllable and visually consistent results across diverse camera operations, outperforming existing approaches in both realism and editability.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 24 of its 39 references that are in the map. First posted on arXiv 11 Oct 2026.

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References · 24

  1. 2024Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolutioncited by 4,234
  2. 2022Instant Neural Graphics Primitives with a Multiresolution Hash Encodingcited by 3,723
  3. 2023AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuningcited by 1,433
  4. 2023MotionCtrl: A Unified and Flexible Motion Controller for Video Generationcited by 580
  5. 2023CoTracker: It is Better to Track Togethercited by 522
  6. 2025VACE: All-in-One Video Creation and Editingcited by 479
  7. 2024CameraCtrl: Enabling Camera Control for Text-to-Video Generationcited by 460
  8. 2023UniPC: A Unified Predictor-Corrector Framework for Fast Sampling of Diffusion Modelscited by 450
  9. 2025ReCamMaster: Camera-Controlled Generative Rendering from A Single Videocited by 287
  10. 2023DragNUWA: Fine-grained Control in Video Generation by Integrating Text, Image, and Trajectorycited by 264
  11. 2024CamCo: Camera-Controllable 3D-Consistent Image-to-Video Generationcited by 166
  12. 2024Tora: Trajectory-oriented Diffusion Transformer for Video Generationcited by 164
12 more
  1. 2026Seedance 2.0: Advancing Video Generation for World Complexitycited by 160
  2. 2024CamI2V: Camera-Controlled Image-to-Video Diffusion Modelcited by 120
  3. 2024TokenHMR: Advancing Human Mesh Recovery with a Tokenized Pose Representationcited by 95
  4. 2024MOFA-Video: Controllable Image Animation via Generative Motion Field Adaptions in Frozen Image-to-Video Diffusion Modelcited by 89
  5. 2025CineMaster: A 3D-Aware and Controllable Framework for Cinematic Text-to-Video Generationcited by 74
  6. 2024Image Conductor: Precision Control for Interactive Video Synthesiscited by 63
  7. 2025RealCam-I2V: Real-World Image-to-Video Generation with Interactive Complex Camera Controlcited by 57
  8. 2025CamCloneMaster: Enabling Reference-based Camera Control for Video Generationcited by 46
  9. 2024AnimateAnything: Consistent and Controllable Animation for Video Generationcited by 35
  10. 2025BEDLAM2.0: Synthetic Humans and Cameras in Motioncited by 25
  11. 2025Perception-as-Control: Fine-grained Controllable Image Animation with 3D-aware Motion Representationcited by 23
  12. 2024AKiRa: Augmentation Kit on Rays for optical video generationcited by 18

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