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SeeThrough3D: Occlusion Aware 3D Control in Text-to-Image Generation
arXiv – CS AI|Vaibhav Agrawal, Rishubh Parihar, Pradhaan Bhat, Ravi Kiran Sarvadevabhatla, R. Venkatesh Babu||7 views
🤖AI Summary
Researchers introduce SeeThrough3D, a new AI model that improves 3D layout-conditioned image generation by explicitly modeling object occlusions. The model uses an occlusion-aware 3D scene representation with translucent boxes to better understand depth relationships and generate more realistic partially occluded objects in synthetic scenes.
Key Takeaways
- →SeeThrough3D addresses occlusion reasoning, a fundamental limitation in current 3D layout-conditioned image generation models.
- →The model introduces an occlusion-aware 3D scene representation using translucent 3D boxes to encode hidden object regions.
- →Researchers use masked self-attention to bind object bounding boxes to textual descriptions, preventing attribute mixing.
- →The model generalizes to unseen object categories and enables precise 3D layout control with realistic occlusions.
- →Training data consists of synthetic datasets with diverse multi-object scenes featuring strong inter-object occlusions.
#3d-generation#computer-vision#text-to-image#occlusion-modeling#machine-learning#synthetic-data#arxiv
Read Original →via arXiv – CS AI
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