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Recapture
Overview :
ReCapture is a method for generating new videos and unique camera trajectories from a single user-provided video. This technology allows us to regenerate the source video from completely different angles with cinematic-level camera movements. ReCapture generates noisy anchor videos with new camera trajectories using multi-view diffusion models or depth-based point cloud rendering. It then refines the anchor video through our proposed masking video fine-tuning technique to produce clean and temporally consistent re-angled videos. The significance of this technology lies in its ability to leverage the strong priors of video models to regenerate visually pleasing and temporally coherent videos from approximate inputs.
Target Users :
Target audience includes video creators, filmmakers, game developers, and professionals who need to generate new perspectives and camera movements from a single video source. ReCapture offers an innovative approach that allows them to create video content with new angles and dynamic camera effects without actually shooting new footage, which is crucial for cost savings and enhancing creative flexibility.
Use Cases
Filmmakers use ReCapture to create different camera angles and movements for film scenes.
Game developers utilize ReCapture technology to generate new promotional videos from game footage.
Video editors use ReCapture to create unique perspectives and dynamic effects for social media content.
Features
- Multi-view diffusion models or depth point cloud rendering: Generate noisy anchor videos with new camera trajectories.
- Masking video fine-tuning technique: Improve video quality by fine-tuning and learning scene dynamics and appearance.
- Scene dynamics learning: Use temporal LoRA to fine-tune on masked anchor videos, learning scene dynamics.
- Scene appearance learning: Use spatial LoRA to fine-tune on enhanced frames of the source video, learning scene appearance.
- Ignore uninformative areas: Focus only on information-rich parts of the anchor video, disregarding missing regions.
- Enhance the prior of the video model: Leverage the strong priors of video models to improve video quality.
- Support complex camera trajectories: Include orbital movements around specific points in the scene, etc.
How to Use
1. Provide a user video as the source.
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2. Define new camera trajectories, including zoom, pan, and tilt.
3. Use multi-view diffusion models or depth point cloud rendering to generate anchor videos with new camera trajectories.
4. Apply masking video fine-tuning techniques to adjust the anchor video, learning the dynamics and appearance of the scene.
5. Regenerate clean and temporally consistent re-angled videos using the fine-tuned model.
6. Check the quality of the final video and make further adjustments if necessary.
7. Export and utilize the generated video content.
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