StreamErase: Towards Ultra-Fast Streaming Video Erasing

Zhuobai Dong; Xiaobing Tu; Xiantao Zhang; Jinkui Ren; Yinggui Wang; Yue Ma; Linfeng Zhang

cs.CVOct 14, 2026

StreamErase: Towards Ultra-Fast Streaming Video Erasing

Authors: Zhuobai Dong, Xiaobing Tu, Xiantao Zhang, Jinkui Ren, Yinggui Wang, Yue Ma, Linfeng Zhang

Organizations: Wuhan University · Alibaba Group · The Hong Kong University of Science and Technology · Shanghai Jiao Tong University

Abstract

Current video object removal methods suffer from prohibitive latency (minutes per video) and non-causal processing that prevents real-time interaction. We present StreamErase, enabling sub-second latency with up to 26 FPS streaming generation on a single GPU. Our approach first augments a text-to-video model with mask input to generate high-quality videos that erase target objects and synthesize coherent backgrounds, but does not perform inference on the fly. As such, we progressively transfer the erasing capability from a powerful bidirectional foundation model to an efficient unidirectional streaming eraser, enabling long-horizon erasing without sacrificing visual fidelity. Several key challenges arise when generating videos over long, potentially infinite horizons: (1) bridging the domain gap from training on finite length and extrapolating to long horizons, (2) sustaining high quality by preventing error accumulation, and (3) maintaining fast inference, without increasing computational cost as the context grows. To address these challenges, we carefully designed sliding-window causal attention with attention sinks. By incorporating self-rollout with attention sinks and KV cache rolling during training, we simulate inference-time extrapolations with a fixed context window, enabling constant-speed generation of arbitrarily long videos. Finally, we establish a dedicated benchmark, LongErase, for streaming long-video object removal. Our model achieves strong performance compared to existing video object erasing models and generalizes well to real-world video scenarios, uniquely enabling infinite-length streaming. With StreamErase, users can input a mask and see the erasing results unfold in real-time, delivering a truly interactive experience.

Figures & tables

Figure 1: Prior video diffusion erasing models typically operate offline to generate fixed-length sequences in parallel (top left). In contrast, our StreamErase enables streaming long-video generation (bottom left), supporting real-time erasing at 26.06 FPS with 0.87s latency (right).

Figure 2: The model architecture of StreamErase. We concatenate the noisy latents with the original input video and masks, consumed by a video inpainting model. Each DiT block performs cross-attention using the fused features $\dot{\bm{x}}_{t}$ as Query and $\bm{e}^{\text{prompt}}$ from Region Guidance as Key/Value, producing attention maps that highlight affected regions. We aggregate attention maps from all blocks, which is supervised by the effect erasing loss $\mathcal{L}_{\text{EE}}$ to focus on the affected area.

Figure 3: Overview of the proposed streaming video erasing framework. Our approach features a three-stage distillation pipeline that transfers erasing capabilities from a bidirectional teacher model to a 1-step causal student model. Each stage utilizes the backbone detailed in Fig. 2 .

Method	FPS	ROSE-Benchmark				VOR-Eval
Method	FPS	PSNR↑	SSIM↑	LPIPS↓	FVD↓	PSNR↑	SSIM↑	LPIPS↓	FVD↓
ObjectClear		29.535	0.920	0.076	742.829	22.403	0.787	0.190	1391.858
OmniPaint		27.569	0.910	0.085	809.645	21.511	0.781	0.201	1439.867
Propainter	3.92	27.200	0.915	0.095	171.020	21.975	0.800	0.225	589.012
DiffuEraser	1.93	26.502	0.898	0.128	167.483	21.946	0.802	0.214	559.497
VACE	0.57	20.805	0.694	0.174	254.117	17.677	0.591	0.294	806.476

Table 1: Quantitative results on ROSE and VOR-Eval. The best and second-best results are highlighted in bold and underlined, respectively.

Figure 4: Qualitative comparison between StreamErase and existing approaches on real-world samples. Our model demonstrates superior ability and effectively handles complex object-environment interactions, including shadows, occlusions, and illumination changes.

Method	Evaluation Scores $\uparrow$					$\mathbf{\delta_{Drift}^{Quality}\downarrow}$
Method	Motion Smoothness	Background Consistency	Temporal Flickering	Subject Consistency	Imaging Quality	$\mathbf{\delta_{Drift}^{Quality}\downarrow}$
Short-Video Comparison (5s)
DiffuEraser	0.968	0.917	0.939	0.947	0.614	-
ProPainter	0.972	0.925	0.935	0.951	0.602	-
ROSE	0.971	0.933	0.922	0.955	0.602	-
EffectErase	0.987	0.937	0.937	0.956	0.604	-

Table 2: Quantitative evaluation on LongErase. Comparison on 5-second clips and long-term performance on 30-second videos. The best score is bolded.

Table 7

Figure 5: Impact of Sparse Attention Patterns. Using longer clips (average 486 frames) from the LongErase, we ablate attention sink size and local window size in extrapolation scenarios. Having at least a single sink chunk is crucial, but more provides marginal benefit, while larger window sizes degrade performance as attending to long-past history allows errors to accumulate in context tokens.

Config	LongErase
Config	LPIPS	$\mathbf{\delta_{Drift}^{Quality}}$	Latency (s)	FPS
Ours base ( c3s1w1 )	0.163	0.05	0.87 $\pm$ 0.04	26.06 $\pm$ 1.62
+ Remove sink ( c3s0w1 )	0.254	0.27	0.85 $\pm$ 0.01	27.28 $\pm$ 1.70
+ Chunk Size 1 ( c1s1w1 )	0.319	0.72	0.42 $\pm$ 0.03	20.70 $\pm$ 2.97
Sliding window	0.181	0.08	0.91 $\pm$ 0.15	22.69 $\pm$ 3.29

Table 5: Long-video streaming design analysis on LongErase. c3s1w1 achieves the best visual quality and long-term stability.

Figure 6: Visualization of long-video eraseing and Streaming Demo. Our method supports flexible length video erasing while preserving high visual fidelity.

Appendix figures & tables4 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 7: Speed and Quality Tradeoffs with Chunk Sizes and Sampling Steps. We visualize the latency-throughput relationship across varying chunk sizes and sampling steps (left), and video quality (LPIPS) across different sampling steps, using our default setup of c3s1w1 (right).

Figure 8: More qualitative comparison between StreamErase and existing approaches on real-world samples. Our model demonstrates superior ability and effectively handles complex object-environment interactions, including shadows, occlusions, and illumination changes.

Figure 9: Qualitative comparison across training stages.

Figure 10: Qualitative Comparison of Long-Video Erasing.

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Abstract

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