Compressing Scene Dynamics: A Generative Approach

• URL: http://arxiv.org/abs/2410.09768v1
• Date: Sun, 13 Oct 2024 07:54:02 GMT
• Title: Compressing Scene Dynamics: A Generative Approach
• Authors: Shanzhi Yin, Zihan Zhang, Bolin Chen, Shiqi Wang, Yan Ye,
• Abstract summary: This paper proposes to learn generative priors from the motion patterns instead of video contents for generative video compression. The priors are derived from small motion dynamics in common scenes such as swinging trees in the wind and floating boat on the sea. A novel generative scene dynamics compression framework is built to realize ultra-low bit-rate communication and high-quality reconstruction for diverse scene contents.
• Score: 26.838900035206613
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper proposes to learn generative priors from the motion patterns instead of video contents for generative video compression. The priors are derived from small motion dynamics in common scenes such as swinging trees in the wind and floating boat on the sea. Utilizing such compact motion priors, a novel generative scene dynamics compression framework is built to realize ultra-low bit-rate communication and high-quality reconstruction for diverse scene contents. At the encoder side, motion priors are characterized into compact representations in a dense-to-sparse manner. At the decoder side, the decoded motion priors serve as the trajectory hints for scene dynamics reconstruction via a diffusion-based flow-driven generator. The experimental results illustrate that the proposed method can achieve superior rate-distortion performance and outperform the state-of-the-art conventional video codec Versatile Video Coding (VVC) on scene dynamics sequences. The project page can be found at https://github.com/xyzysz/GNVDC.

Related papers

• Generative Human Video Compression with Multi-granularity Temporal Trajectory Factorization [13.341123726068652]
  We propose a novel Multi-granularity Temporal Trajectory Factorization framework for generative human video compression. Experimental results show that proposed method outperforms latest generative models and the state-of-the-art video coding standard Versatile Video Coding.
  arXiv  Detail & Related papers  (2024-10-14T05:34:32Z)
• VDG: Vision-Only Dynamic Gaussian for Driving Simulation [112.6139608504842]
  We introduce self-supervised VO into our pose-free dynamic Gaussian method (VDG) VDG can work with only RGB image input and construct dynamic scenes at a faster speed and larger scenes compared with the pose-free dynamic view-synthesis method. Our results show favorable performance over the state-of-the-art dynamic view synthesis methods.
  arXiv  Detail & Related papers  (2024-06-26T09:29:21Z)
• VMC: Video Motion Customization using Temporal Attention Adaption for Text-to-Video Diffusion Models [58.93124686141781]
  Video Motion Customization (VMC) is a novel one-shot tuning approach crafted to adapt temporal attention layers within video diffusion models. Our approach introduces a novel motion distillation objective using residual vectors between consecutive frames as a motion reference. We validate our method against state-of-the-art video generative models across diverse real-world motions and contexts.
  arXiv  Detail & Related papers  (2023-12-01T06:50:11Z)
• MoVideo: Motion-Aware Video Generation with Diffusion Models [97.03352319694795]
  We propose a novel motion-aware generation (MoVideo) framework that takes motion into consideration from two aspects: video depth and optical flow. MoVideo achieves state-of-the-art results in both text-to-video and image-to-video generation, showing promising prompt consistency, frame consistency and visual quality.
  arXiv  Detail & Related papers  (2023-11-19T13:36:03Z)
• StyleInV: A Temporal Style Modulated Inversion Network for Unconditional Video Generation [73.54398908446906]
  We introduce a novel motion generator design that uses a learning-based inversion network for GAN. Our method supports style transfer with simple fine-tuning when the encoder is paired with a pretrained StyleGAN generator.
  arXiv  Detail & Related papers  (2023-08-31T17:59:33Z)
• VNVC: A Versatile Neural Video Coding Framework for Efficient Human-Machine Vision [59.632286735304156]
  It is more efficient to enhance/analyze the coded representations directly without decoding them into pixels. We propose a versatile neural video coding (VNVC) framework, which targets learning compact representations to support both reconstruction and direct enhancement/analysis.
  arXiv  Detail & Related papers  (2023-06-19T03:04:57Z)
• LaMD: Latent Motion Diffusion for Video Generation [69.4111397077229]
  latent motion diffusion (LaMD) framework consists of a motion-decomposed video autoencoder and a diffusion-based motion generator. Results show that LaMD generates high-quality videos with a wide range of motions, from dynamics to highly controllable movements.
  arXiv  Detail & Related papers  (2023-04-23T10:32:32Z)
• Scene Matters: Model-based Deep Video Compression [13.329074811293292]
  We propose a model-based video compression (MVC) framework that regards scenes as the fundamental units for video sequences. Our proposed MVC directly models novel intensity variation of the entire video sequence in one scene, seeking non-redundant representations instead of reducing redundancy. Our method achieves up to a 20% reduction compared to the latest video standard H.266 and is more efficient in decoding than existing video coding strategies.
  arXiv  Detail & Related papers  (2023-03-08T13:15:19Z)
• MotionVideoGAN: A Novel Video Generator Based on the Motion Space Learned from Image Pairs [16.964371778504297]
  We present MotionVideoGAN, a novel video generator synthesizing videos based on the motion space learned by pre-trained image pair generators. Motion codes help us edit images within the motion space since the edited image shares the same contents with the other unchanged one in image pairs. Our approach achieves state-of-the-art performance on the most complex video dataset ever used for unconditional video generation evaluation, UCF101.
  arXiv  Detail & Related papers  (2023-03-06T05:52:13Z)

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.