Gen Through the Lens: Camera-Controllable Video Generation via Cross-Coordinate Conditioning Fusion
cs.CV, cs.AI, cs.GR, cs.LG
Submitted: 2026-10-11
Updated: 2026-10-11
Sources
- ReCamMaster: Camera-Controlled Generative Rendering from A Single Video
- Perception-as-Control: Fine-grained Controllable Image Animation with 3D-aware Motion Representation
- Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling
- I2VControl-Camera: Precise Video Camera Control with Adjustable Motion Strength
- Motion Prompting: Controlling Video Generation with Motion Trajectories
- AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning
- CameraCtrl: Enabling Camera Control for Text-to-Video Generation
- VACE: All-in-One Video Creation and Editing
- MapAnything: Universal Feed-Forward Metric 3D Reconstruction
- AnimateAnything: Consistent and Controllable Animation for Video Generation
- SAM 2: Segment Anything in Images and Videos
- Seedance 2.0: Advancing Video Generation for World Complexity
- BEDLAM2.0: Synthetic Humans and Cameras in Motion
- Wan: Open and Advanced Large-Scale Video Generative Models
- Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
- MoGe-2: Accurate Monocular Geometry with Metric Scale and Sharp Details
- $\pi^3$: Permutation-Equivariant Visual Geometry Learning
- DragAnything: Motion Control for Anything using Entity Representation
- CamCo: Camera-Controllable 3D-Consistent Image-to-Video Generation
- DragNUWA: Fine-grained Control in Video Generation by Integrating Text, Image, and Trajectory
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