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cloud.google.com·8h ago
AI Research
arXiv CS.AI·Aug 31·5 min read
Video Generative Models as Geometry Learner
TL;DR
GeoNeXt is a novel framework that repurposes pretrained video generative models for geometry estimation, achieving superior performance in depth and surface normal estimation with significantly less training data.
✦ Why It Matters
Consider adopting GeoNeXt for your geometry estimation tasks to reduce data requirements and improve accuracy.
Key Takeaways
- Explore GeoNeXt for efficient geometry estimation tasks.
- Utilize pretrained video models to enhance data efficiency.
- Leverage joint modeling for improved depth and surface normal accuracy.
- Evaluate performance against state-of-the-art methods with less training data.
How It Works
GeoNeXt repurposes video generative models by framing geometry estimation as a next-frame prediction task. This allows the model to learn from the temporal structure of video data, effectively capturing the relationships between images and their corresponding geometric properties.
By integrating knowledge from video, GeoNeXt can jointly model images and geometry targets, leading to more effective learning with less labeled data.