EditaLive! Unified Character Video Editing for Live Streaming

Zhiyuan Li; Chi-Man Pun; Peng-Tao Jiang; Bo Li; Xiaodong Cun

cs.CVAug 27, 2026

EditaLive! Unified Character Video Editing for Live Streaming

Authors: Zhiyuan Li, Chi-Man Pun, Peng-Tao Jiang, Bo Li, Xiaodong Cun

Organizations: University of Macau · GVC Lab, Great Bay University · vivo BlueImage Lab

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.

Figures & tables

Figure 2: Comparison of three character video editing paradigms. (a) General Video-to-Video Editing directly transforms a source video $V$ into an edited video $V^{\prime}$ , but often relies on costly synthetic video pairs that may contain facial-expression mismatches and weakened human details. (b) Cascaded image editing and animation first edits the source appearance $A$ into $A^{\prime}$ and then animates it using motion $M$ , introducing cascaded latency and potential long-term appearance drift. (c) EditaLive unifies appearance editing and motion-conditioned generation, learning from edited reference images and motion-aligned real videos to preserve facial expressions and body motion while enabling stable, low-latency streaming editing.

Figure 3: Overview of the three-stage training pipeline of EditaLive. (a) Appearance–Motion Decoupled Editing reformulates character video editing as appearance editing with explicit body and facial motion preservation. (b) Causal Streaming Adaptation converts bidirectional modeling into causal generation using clean historical context and causal attention. (c) Aligned Self-Rollout Distillation compresses the model into a two-step sampler, where Align Forcing and Fixed RoPE align training with streaming inference, and FPSA improves long-term appearance stability.

Figure 4: Construction pipeline of CharEdit-50K. (Left) Existing video editing datasets often contain facial expression inconsistencies. (Right) Our pipeline restricts synthetic editing to the reference image while retaining its authentic source video as motion-aligned supervision. (1) High-quality reference frames are selected based on face and hand quality. (2) GPT-5.5 generates instructions covering add–remove, remove–add, change–restore, and stylization, followed by category and color balancing. (3) Qwen-Image-Edit and Nano Banana 2 synthesize candidate images, which are filtered for edit accuracy, target clarity, character consistency, and reverse feasibility. Each retained bidirectional sample contains an edited image, its reverse instruction, and the corresponding source video.

Figure 5: Comparison of Self Forcing and Align Forcing under two-step distillation. (a) Self Forcing couples gradient update with rollout termination, causing an intermediate prediction to be cached when the rollout stops early. This creates a mismatch with inference, which always caches the final prediction, and requires $2.5$ NFEs on average. (b) Align Forcing applies gradients only at the sampled step but completes the remaining rollout with stop-gradient. The KV cache is therefore always constructed from the final prediction, matching inference while requiring two NFEs. (c) During streaming inference, each chunk propagates its final KV cache after two-step denoising, with an additional $t_{0}$ forward pass for the first chunk to initialize the attention sink.

Figure 6: Qualitative comparisons. EditaLive faithfully follows the editing instructions while preserving the source facial expressions, body poses, and scene structure across frames.

Method	#Params	Character Consistency			VLM Evaluation				Video Quality	Efficiency
Method	#Params	ID-SIM $\uparrow$	AED $\downarrow$	APD $\downarrow$	TA $\uparrow$	EQ $\uparrow$	BC $\uparrow$	SR $\uparrow$	Pick Score $\uparrow$	FPS $\uparrow$	Latency $\downarrow$
LiveEdit*	1.3B	–	–	–	1.542	1.949	1.640	0.393	19.26	17.25	0.696
SANA-Stream.	2B	0.379	0.629	0.381	2.200	1.777	1.582	0.428	19.46	31.20	0.772
Stream.V2	14B	0.060	0.885	0.485	1.316	1.869	0.473	0.027	19.92	20.96	0.191
LucyEdit*	5B	–	–	–	1.604	1.797	2.121	0.326	18.92	3.083	26.27
UniVideo	13B	0.592	0.605	0.447	2.377	2.182	1.866	0.533	19.54	0.119	680.9

Table 1: Quantitative comparisons on CharEdit-Bench-S. Numbers in red and blue indicate the best and the second-best results, respectively. APD multiplied by

10

. TA, EQ, BC and SR denote text alignment, edit quality, background consistency, and success rate, respectively. *LiveEdit and LucyEdit do not support global style editing. We therefore omit their character-consistency metrics, as these scores are not comparable when the requested edit is not successfully performed.

Figure 7: Ablation study on causal streaming adaptation and aligned self-rollout distillation.

Method	Character Consistency			VLM Evaluation				Video Quality	Temporal Consistency
Method	ID-SIM $\uparrow$	AED $\downarrow$	APD $\downarrow$	TA $\uparrow$	EQ $\uparrow$	BC $\uparrow$	SR $\uparrow$	Pick Score $\uparrow$	CLIP $\uparrow$	DINO $\uparrow$
w/o Align Forcing	0.252	0.711	0.338	2.703	2.567	1.306	0.367	20.03	98.62	99.07
w/o Fixed RoPE	0.381	0.618	0.120	2.728	2.639	1.872	0.617	19.91	98.50	98.95
w/o FPSA	0.455	0.609	0.116	2.737	2.600	2.105	0.800	19.85	98.46	98.88
w/o FP	0.466	0.604	0.114	2.725	2.622	2.139	0.833	19.84	98.42	98.89
Ours	0.492	0.576	0.109	2.761	2.678	2.183	0.817	19.78	98.51	98.96

Table 2: Ablation study on aligned self-rollout distillation.

Appendix figures & tables12 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 10: Qualitative comparisons on CharEdit-Bench-L. EditaLive consistently maintains the requested edit and character appearance while following the source motion.

Method	Character Consistency			VLM Evaluation				Video Quality	Temporal Consistency
Method	ID-SIM $\uparrow$	AED $\downarrow$	APD $\downarrow$	TA $\uparrow$	EQ $\uparrow$	BC $\uparrow$	SR $\uparrow$	Pick Score $\uparrow$	CLIP $\uparrow$	DINO $\uparrow$
LiveEdit*	–	–	–	1.583	1.700	1.044	0.017	18.91	97.36	98.38
SANA-Stream.	0.233	0.725	0.402	2.272	1.938	1.121	0.233	19.62	97.28	98.25
Stream.V2	0.049	0.876	0.329	1.431	2.064	0.638	0.100	19.70	98.42	99.17
Qwen.+Wan.	0.398	0.659	0.126	2.733	2.304	1.776	0.750	19.69	98.30	98.65
Ours	0.492	0.576	0.109	2.761	2.678	2.183	0.817	19.78	98.51	98.96

Appendix

Table 3: Quantitative comparisons on CharEdit-Bench-L. Numbers in red and blue indicate the best and the second-best results, respectively. APD multiplied by

10

. *LiveEdit does not support global style editing. We therefore omit its character-consistency metrics, as these scores are not comparable when the requested edit is not successfully performed.

Figure 11: Cross-character editing results.

Figure 12: Limitations of EditaLive. The editing prompt is “ Transform it into a Snoopy style ”.

Figure 13: More comparisons between baselines and our EditaLive (1/4).

Figure 14: More comparisons between baselines and our EditaLive (2/4).

Figure 15: Long-video comparison results (1/2).

Figure 16: More comparisons between baselines and our EditaLive (3/4).

Figure 17: Long-video comparison results (2/2).

Figure 18: More comparisons between baselines and our EditaLive (4/4).

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