EditaLive! Unified Character Video Editing for Live Streaming

• URL: http://arxiv.org/abs/2608.27123v1
• Date: Thu, 27 Aug 2026 13:37:45 GMT
• Title: EditaLive! Unified Character Video Editing for Live Streaming
• Abstract summary: EditaLive is a novel framework for real-time streaming character video editing.<n>It delivers state-of-the-art editing performance with faithful preservation of facial expressions and low-latency real-time streaming inference.
• Score: 59.93029261408041
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Conventional video editing primarily focuses on scene-level content, whereas live streaming places greater emphasis on the human subject. However, directly applying existing video-editing methods to human-centric live streaming remains challenging, as they may introduce facial-expression inconsistencies and typically depend on multiple offline inference steps, making them unsuitable for real-time interaction. We propose EditaLive, a novel framework for real-time streaming character video editing. In detail, we start from a pretrained image animation model (Wan-Animate), which naturally decouples appearance from motion, and repurpose it as the base model for instruction-based human-centric video editing by reference frame editing and video reconstruction via the collected CharEdit-50K dataset. Besides, we adapt the model from offline bidirectional to causal streaming generation, and design an aligned self-rollout distillation strategy that compresses the model into a two-step sampler, where fixed RoPE and align forcing reduce training--inference discrepancies, and first-frame preserved sparse attention filters redundant historical information to mitigate appearance drift. Extensive experiments demonstrate that EditaLive delivers state-of-the-art editing performance with faithful preservation of facial expressions and low-latency real-time streaming inference.

Related papers

• EditStream: A Unified Autoregressive Framework for Interactive Video Generation and Editing [67.99022753510475]
  EditStream is a unified framework for interactive video generation and editing.<n>It unifies multiple video creation and manipulation tasks within a single DiT-based model.<n>It supports Text-to-Video, Image-to-Video, Video-to-Video, Editing propagation, Reference-guided Video Editing, and Camera Pose Change.
  arXiv  Detail & Related papers  (2026-08-16T07:27:18Z)
• LiveEdit: Towards Real-Time Diffusion-Based Streaming Video Editing [19.90420626930003]
  We present a novel streaming video editing framework that performs causal, frame-by-frame editing with strong content preservation and real-time responsiveness.<n>Our method achieves state-of-the-art visual quality among streaming baselines while drastically boosting inference speed to 12.66 FPS, making it suitable for interactive and augmented reality applications.
  arXiv  Detail & Related papers  (2026-06-25T08:24:03Z)
• LIVE: Leveraging Image Manipulation Priors for Instruction-based Video Editing [45.1196749483108]
  We propose LIVE, a joint training framework that leverages large-scale, high-quality image editing data alongside video datasets to bolster editing capabilities.<n>We introduce a frame-wise token noise strategy, which treats the latents of specific frames as reasoning tokens, leveraging large pretrained video generative models to create plausible temporal transformations.<n>We curate a comprehensive evaluation benchmark encompassing over 60 challenging tasks that are prevalent in image editing but scarce in existing video datasets.
  arXiv  Detail & Related papers  (2026-04-18T15:09:01Z)
• ImVideoEdit: Image-learning Video Editing via 2D Spatial Difference Attention Blocks [46.442660004277734]
  We propose ImVideoEdit, an efficient framework that learns video editing capabilities entirely from image pairs.<n>By freezing the pre-trained 3D attention modules and treating images as single-frame videos, we decouple the 2D spatial learning process to help preserve the original temporal dynamics.<n>Despite training on only 13K image pairs for 5 epochs with exceptionally low computational overhead, ImVideoEdit achieves editing fidelity and temporal consistency comparable to larger models trained on extensive video datasets.
  arXiv  Detail & Related papers  (2026-04-09T08:22:09Z)
• EditYourself: Audio-Driven Generation and Manipulation of Talking Head Videos with Diffusion Transformers [3.3508228801277853]
  We introduce EditYourself, a DiTT-based framework for audio-driven video-to-videoV editing.<n>It enables transcript-based modification of talking videos, including the seamless addition, removal, and retiming of visually spoken content.<n>This represents a step toward generative video models as practical tools for professional video post-production.
  arXiv  Detail & Related papers  (2026-01-29T18:49:27Z)
• DeCo: Decoupled Human-Centered Diffusion Video Editing with Motion Consistency [66.49423641279374]
  We introduce DeCo, a novel video editing framework specifically designed to treat humans and the background as separate editable targets. We propose a decoupled dynamic human representation that utilizes a human body prior to generate tailored humans. We extend the calculation of score distillation sampling into normal space and image space to enhance the texture of humans during the optimization.
  arXiv  Detail & Related papers  (2024-08-14T11:53:40Z)
• Zero-Shot Video Editing through Adaptive Sliding Score Distillation [51.57440923362033]
  This study proposes a novel paradigm of video-based score distillation, facilitating direct manipulation of original video content. We propose an Adaptive Sliding Score Distillation strategy, which incorporates both global and local video guidance to reduce the impact of editing errors.
  arXiv  Detail & Related papers  (2024-06-07T12:33:59Z)
• FateZero: Fusing Attentions for Zero-shot Text-based Video Editing [104.27329655124299]
  We propose FateZero, a zero-shot text-based editing method on real-world videos without per-prompt training or use-specific mask. Our method is the first one to show the ability of zero-shot text-driven video style and local attribute editing from the trained text-to-image model.
  arXiv  Detail & Related papers  (2023-03-16T17:51:13Z)
• Edit-A-Video: Single Video Editing with Object-Aware Consistency [49.43316939996227]
  We propose a video editing framework given only a pretrained TTI model and a single text, video> pair, which we term Edit-A-Video. The framework consists of two stages: (1) inflating the 2D model into the 3D model by appending temporal modules tuning and on the source video (2) inverting the source video into the noise and editing with target text prompt and attention map injection. We present extensive experimental results over various types of text and videos, and demonstrate the superiority of the proposed method compared to baselines in terms of background consistency, text alignment, and video editing quality.
  arXiv  Detail & Related papers  (2023-03-14T14:35:59Z)

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.