SemCam: Semantic Camera Motion Control for Video Generation
cs.CV
Submitted: 2026-10-04
Updated: 2026-10-04
Project page: https://semcam.github.io
Sources
- Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets
- GS-DiT: Advancing Video Generation with Pseudo 4D Gaussian Fields through Efficient Dense 3D Point Tracking
- ReCamMaster: Camera-Controlled Generative Rendering from A Single Video
- SAM 3: Segment Anything with Concepts
- Control-A-Video: Controllable Text-to-Video Diffusion Models with Motion Prior and Reward Feedback Learning
- VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models
- TokenVerse: Versatile Multi-concept Personalization in Token Modulation Space
- LTX-2: Efficient Joint Audio-Visual Foundation Model
- LoRA: Low-Rank Adaptation of Large Language Models
- In-Context LoRA for Diffusion Transformers
- CameraCtrl: Enabling Camera Control for Text-to-Video Generation
- Depth Anything 3: Recovering the Visual Space from Any Views
- Vista4D: Video Reshooting with 4D Point Clouds
- Spanning the Visual Analogy Space with a Weight Basis of LoRAs
- Wan: Open and Advanced Large-Scale Video Generative Models
- LiON-LoRA: Rethinking LoRA Fusion to Unify Controllable Spatial and Temporal Generation for Video Diffusion
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