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Camera and human motion controls have been extensively studied for video generation, but existing approaches typically address them separately, suffering from limited data with high-quality annotations for both aspects.
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Vbench++: Comprehensive and versatile benchmark suite for video generative models
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Perception-as-Control: Fine-grained Controllable Image Animation with 3D-aware Motion Representation
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I2VControl-Camera: Precise Video Camera Control with Adjustable Motion Strength. In International Conference on Learning Representations
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Motion Prompting: Controlling Video Generation with Motion Trajectories. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition
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Diffusion as Shader: 3D-aware Video Diffusion for Versatile Video Generation Control
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CameraCtrl II: Dynamic Scene Exploration via Camera-controlled Video Diffusion Models
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Wan: Open and Advanced Large-Scale Video Generative Models
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RealisDance-DiT: Simple yet Strong Baseline towards Controllable Character Animation in the Wild
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STABLE VIRTUAL CAMERA: Generative View Synthesis with Diffusion Models
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