NeRV-Diffusion: Diffuse Implicit Neural Representations for Video Synthesis

• URL: http://arxiv.org/abs/2509.24353v1
• Date: Mon, 29 Sep 2025 06:53:08 GMT
• Title: NeRV-Diffusion: Diffuse Implicit Neural Representations for Video Synthesis
• Authors: Yixuan Ren, Hanyu Wang, Hao Chen, Bo He, Abhinav Shrivastava,
• Abstract summary: NeRV-Diffusion is an implicit latent video diffusion model that synthesizes videos via generating neural network weights.<n>In contrast to traditional video tokenizers that encode videos into frame-wise feature maps, NeRV-Diffusion compresses and generates a video holistically as a unified neural network.
• Score: 48.35964370809449
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present NeRV-Diffusion, an implicit latent video diffusion model that synthesizes videos via generating neural network weights. The generated weights can be rearranged as the parameters of a convolutional neural network, which forms an implicit neural representation (INR), and decodes into videos with frame indices as the input. Our framework consists of two stages: 1) A hypernetworkbased tokenizer that encodes raw videos from pixel space to neural parameter space, where the bottleneck latent serves as INR weights to decode. 2) An implicit diffusion transformer that denoises on the latent INR weights. In contrast to traditional video tokenizers that encode videos into frame-wise feature maps, NeRV-Diffusion compresses and generates a video holistically as a unified neural network. This enables efficient and high-quality video synthesis via obviating temporal cross-frame attentions in the denoiser and decoding video latent with dedicated decoders. To achieve Gaussian-distributed INR weights with high expressiveness, we reuse the bottleneck latent across all NeRV layers, as well as reform its weight assignment, upsampling connection and input coordinates. We also introduce SNR-adaptive loss weighting and scheduled sampling for effective training of the implicit diffusion model. NeRV-Diffusion reaches superior video generation quality over previous INR-based models and comparable performance to most recent state-of-the-art non-implicit models on real-world video benchmarks including UCF-101 and Kinetics-600. It also brings a smooth INR weight space that facilitates seamless interpolations between frames or videos.

Related papers

• NERV++: An Enhanced Implicit Neural Video Representation [11.25130799452367]
  We introduce neural representations for videos NeRV++, an enhanced implicit neural video representation. NeRV++ is more straightforward yet effective enhancement over the original NeRV decoder architecture. We evaluate our method on UVG, MCL JVC, and Bunny datasets, achieving competitive results for video compression with INRs.
  arXiv  Detail & Related papers  (2024-02-28T13:00:32Z)
• Boosting Neural Representations for Videos with a Conditional Decoder [28.073607937396552]
  Implicit neural representations (INRs) have emerged as a promising approach for video storage and processing. This paper introduces a universal boosting framework for current implicit video representation approaches.
  arXiv  Detail & Related papers  (2024-02-28T08:32:19Z)
• Progressive Fourier Neural Representation for Sequential Video Compilation [75.43041679717376]
  Motivated by continual learning, this work investigates how to accumulate and transfer neural implicit representations for multiple complex video data over sequential encoding sessions. We propose a novel method, Progressive Fourier Neural Representation (PFNR), that aims to find an adaptive and compact sub-module in Fourier space to encode videos in each training session. We validate our PFNR method on the UVG8/17 and DAVIS50 video sequence benchmarks and achieve impressive performance gains over strong continual learning baselines.
  arXiv  Detail & Related papers  (2023-06-20T06:02:19Z)
• DNeRV: Modeling Inherent Dynamics via Difference Neural Representation for Videos [53.077189668346705]
  Difference Representation for Videos (eRV) We analyze this from the perspective of limitation function fitting and the importance of frame difference. DNeRV achieves competitive results against the state-of-the-art neural compression approaches.
  arXiv  Detail & Related papers  (2023-04-13T13:53:49Z)
• HNeRV: A Hybrid Neural Representation for Videos [56.492309149698606]
  Implicit neural representations store videos as neural networks. We propose a Hybrid Neural Representation for Videos (HNeRV) With content-adaptive embeddings and re-designed architecture, HNeRV outperforms implicit methods in video regression tasks.
  arXiv  Detail & Related papers  (2023-04-05T17:55:04Z)
• Towards Scalable Neural Representation for Diverse Videos [68.73612099741956]
  Implicit neural representations (INR) have gained increasing attention in representing 3D scenes and images. Existing INR-based methods are limited to encoding a handful of short videos with redundant visual content. This paper focuses on developing neural representations for encoding long and/or a large number of videos with diverse visual content.
  arXiv  Detail & Related papers  (2023-03-24T16:32:19Z)
• Scalable Neural Video Representations with Learnable Positional Features [73.51591757726493]
  We show how to train neural representations with learnable positional features (NVP) that effectively amortize a video as latent codes. We demonstrate the superiority of NVP on the popular UVG benchmark; compared with prior arts, NVP not only trains 2 times faster (less than 5 minutes) but also exceeds their encoding quality as 34.07rightarrow$34.57 (measured with the PSNR metric)
  arXiv  Detail & Related papers  (2022-10-13T08:15:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.