Live2Diff: Live Stream Translation via Uni-directional Attention in Video Diffusion Models

• URL: http://arxiv.org/abs/2407.08701v1
• Date: Thu, 11 Jul 2024 17:34:51 GMT
• Title: Live2Diff: Live Stream Translation via Uni-directional Attention in Video Diffusion Models
• Authors: Zhening Xing, Gereon Fox, Yanhong Zeng, Xingang Pan, Mohamed Elgharib, Christian Theobalt, Kai Chen,
• Abstract summary: Large Language Models have shown remarkable efficacy in generating streaming data such as text and audio. We present Live2Diff, the first attempt at designing a video diffusion model with uni-directional temporal attention, specifically targeting live streaming video translation.
• Score: 64.2445487645478
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models have shown remarkable efficacy in generating streaming data such as text and audio, thanks to their temporally uni-directional attention mechanism, which models correlations between the current token and previous tokens. However, video streaming remains much less explored, despite a growing need for live video processing. State-of-the-art video diffusion models leverage bi-directional temporal attention to model the correlations between the current frame and all the surrounding (i.e. including future) frames, which hinders them from processing streaming videos. To address this problem, we present Live2Diff, the first attempt at designing a video diffusion model with uni-directional temporal attention, specifically targeting live streaming video translation. Compared to previous works, our approach ensures temporal consistency and smoothness by correlating the current frame with its predecessors and a few initial warmup frames, without any future frames. Additionally, we use a highly efficient denoising scheme featuring a KV-cache mechanism and pipelining, to facilitate streaming video translation at interactive framerates. Extensive experiments demonstrate the effectiveness of the proposed attention mechanism and pipeline, outperforming previous methods in terms of temporal smoothness and/or efficiency.

Related papers

• Ouroboros-Diffusion: Exploring Consistent Content Generation in Tuning-free Long Video Diffusion [116.40704026922671]
  First-in-first-out (FIFO) video diffusion, built on a pre-trained text-to-video model, has recently emerged as an effective approach for tuning-free long video generation. We propose Ouroboros-Diffusion, a novel video denoising framework designed to enhance structural and content (subject) consistency.
  arXiv  Detail & Related papers  (2025-01-15T18:59:15Z)
• RepVideo: Rethinking Cross-Layer Representation for Video Generation [53.701548524818534]
  We propose RepVideo, an enhanced representation framework for text-to-video diffusion models. By accumulating features from neighboring layers to form enriched representations, this approach captures more stable semantic information. Our experiments demonstrate that our RepVideo not only significantly enhances the ability to generate accurate spatial appearances, but also improves temporal consistency in video generation.
  arXiv  Detail & Related papers  (2025-01-15T18:20:37Z)
• StreamChat: Chatting with Streaming Video [85.02875830683637]
  StreamChat is a novel approach that enhances the interaction capabilities of Large Multimodal Models with streaming video content. We introduce a flexible and efficient crossattention-based architecture to process dynamic streaming inputs. We construct a new dense instruction dataset to facilitate the training of streaming interaction models.
  arXiv  Detail & Related papers  (2024-12-11T18:59:54Z)
• Optical-Flow Guided Prompt Optimization for Coherent Video Generation [51.430833518070145]
  We propose a framework called MotionPrompt that guides the video generation process via optical flow. We optimize learnable token embeddings during reverse sampling steps by using gradients from a trained discriminator applied to random frame pairs. This approach allows our method to generate visually coherent video sequences that closely reflect natural motion dynamics, without compromising the fidelity of the generated content.
  arXiv  Detail & Related papers  (2024-11-23T12:26:52Z)
• FRESCO: Spatial-Temporal Correspondence for Zero-Shot Video Translation [85.29772293776395]
  We introduce FRESCO, intra-frame correspondence alongside inter-frame correspondence to establish a more robust spatial-temporal constraint. This enhancement ensures a more consistent transformation of semantically similar content across frames. Our approach involves an explicit update of features to achieve high spatial-temporal consistency with the input video.
  arXiv  Detail & Related papers  (2024-03-19T17:59:18Z)
• APLA: Additional Perturbation for Latent Noise with Adversarial Training Enables Consistency [9.07931905323022]
  We propose a novel text-to-video (T2V) generation network structure based on diffusion models. Our approach only necessitates a single video as input and builds upon pre-trained stable diffusion networks. We leverage a hybrid architecture of transformers and convolutions to compensate for temporal intricacies, enhancing consistency between different frames within the video.
  arXiv  Detail & Related papers  (2023-08-24T07:11:00Z)
• Real-time Streaming Video Denoising with Bidirectional Buffers [48.57108807146537]
  Real-time denoising algorithms are typically adopted on the user device to remove the noise involved during the shooting and transmission of video streams. Recent multi-output inference works propagate the bidirectional temporal feature with a parallel or recurrent framework. We propose a Bidirectional Streaming Video Denoising framework, to achieve high-fidelity real-time denoising for streaming videos with both past and future temporal receptive fields.
  arXiv  Detail & Related papers  (2022-07-14T14:01:03Z)

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.