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NeRVs-desh.github.io · Low evidenceNeRV represents a video as an implicit neural function f_theta that maps a frame index t to the corresponding RGB frame v_t, using positional encoding and a mix of MLPs and ConvNets. This representation aims to enable efficient video encoding/decoding and potential compression, trained with a combination of L1 and SSIM losses and augmented by model optimization techniques like pruning, quantization, and entropy coding. The project includes reproducing NeRV results on standard videos, applying it to datasets such as Big Buck Bunny and Cholec80, and exploring hyperparameter variations and algorithmic variants to evaluate performance.
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Sources
arxiv.org shapes more of what AI says about NeRV than any other source, at 100% of its citations.