Scene Representation Transformer: Geometry-Free Novel View Synthesis Through Set-Latent Scene Representations
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Learning-based view synthesis has been explored through a purely data-driven approach using scalable Transformer architectures, bypassing the need for explicit 3D prior knowledge.
“This bypasses the need for explicit 3D prior knowledge (Rombach et al., 2021; Sajjadi et al., 2022): instead of relying on neural rendering applied to 3D representations, novel views are synthesized directly by neural networks conditioned on camera inputs (Tatarchenko et al., 2016).”
From Cosmos World Foundation Model Platform for Physical AI (2025) · cites this paper as Sajjadi et al., 2022
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Cites
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Cited by
Sorted by citations
Cosmos World Foundation Model Platform for Physical AI
NVIDIA, :, Agarwal, N., Ali, A., Bala, M., Balaji, Y., Barker, E., Cai, T., Chattopadhyay, P., Chen, Y., et al. (2025) · arXiv · doi:10.48550/arxiv.2501.03575 · Cited by 13 · see citing works
Object-Centric Scene Representations Using Active Inference
Van de Maele, T., Verbelen, T., Mazzaglia, P., Ferraro, S., & Dhoedt, B. (2024) · Neural Computation · doi:10.1162/neco_a_01637 · Cited by 5 · see citing works
“Object Scene Representation Transformer (Sajjadi et al., 2022a) combines slot attention with scene representation transformers (Sajjadi et al., 2022b) to learn object-centric representations from a set of multiple viewpoints of a scene in an end-to-end fashion.”
Background
- Authors
- Sajjadi, M. S. M., Meyer, H., Pot, E., Bergmann, U., Greff, K., Radwan, N., Vora, S., Lucic, M., Duckworth, D., Dosovitskiy, A., Uszkoreit, J., Funkhouser, T., & Tagliasacchi, A.
- Affiliations
- Google Research
- Simon Fraser University
- Venue
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2022-06
- Pages
- 6219-6228
- Cited by
- 133
- References
- 6
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