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 # CLiFT: Compressive Light-Field Tokens for Compute-Efficient and Adaptive Neural Rendering
 
-Pretrained checkpoints for **CLiFT** ([arXiv](https://arxiv.org/abs/2507.08776), [project page](https://clift-nvs.github.io/), [code](https://github.com/eric-zqwang/CLiFT)).
+Pretrained checkpoints for **CLiFT** (NeurIPS 2025 spotlight; [arXiv](https://arxiv.org/abs/2507.08776), [project page](https://clift-nvs.github.io/), [code](https://github.com/eric-zqwang/CLiFT)).
 
 CLiFT represents a scene as compressed light-field tokens (CLiFTs): an encoder tokenizes the input views, latent K-means selects centroid tokens, and a neural condenser aggregates each cluster into its centroid. A single trained model supports compute-adaptive rendering — fewer tokens for lower storage/compute, more tokens for higher quality.