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Looking Backward: Streaming Video-to-Video Translation with Feature Banks (2405.15757v3)

Published 24 May 2024 in cs.CV and cs.MM

Abstract: This paper introduces StreamV2V, a diffusion model that achieves real-time streaming video-to-video (V2V) translation with user prompts. Unlike prior V2V methods using batches to process limited frames, we opt to process frames in a streaming fashion, to support unlimited frames. At the heart of StreamV2V lies a backward-looking principle that relates the present to the past. This is realized by maintaining a feature bank, which archives information from past frames. For incoming frames, StreamV2V extends self-attention to include banked keys and values and directly fuses similar past features into the output. The feature bank is continually updated by merging stored and new features, making it compact but informative. StreamV2V stands out for its adaptability and efficiency, seamlessly integrating with image diffusion models without fine-tuning. It can run 20 FPS on one A100 GPU, being 15x, 46x, 108x, and 158x faster than FlowVid, CoDeF, Rerender, and TokenFlow, respectively. Quantitative metrics and user studies confirm StreamV2V's exceptional ability to maintain temporal consistency.

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Citations (4)
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Summary

  • The paper presents a streaming approach, StreamV2V, that processes video in real time at 20 FPS, outperforming methods like FlowVid and CoDeF by significant margins.
  • The paper achieves temporal consistency by employing a backward-looking feature bank that archives past frame details without heavy computational loads.
  • The paper integrates diffusion models seamlessly, with user studies and quantitative metrics (CLIP score, warp error) confirming over 70–80% preference win rates.

StreamV2V: Real-Time Video-to-Video Translation for Streaming Input Using Diffusion Models

Recent advancements in diffusion models have spurred significant innovations in image and video generation tasks. The paper introduces StreamV2V, an efficient and versatile real-time video-to-video (V2V) translation model utilizing a diffusion approach. Unlike traditional V2V methods that process video frames in batches, StreamV2V adopts a streaming paradigm to handle unlimited frames, leveraging a backward-looking mechanism to maintain temporal consistency.

Key Contributions

  1. Streaming Video Processing:
    • StreamV2V can process streaming video in real time at 20 frames per second (FPS) on a single A100 GPU. This processing speed surpasses several existing methods—FlowVid, CoDeF, Rerender, and TokenFlow—by remarkable factors of 15, 46, 108, and 158 times, respectively.
  2. Backward-Looking Principle:
    • The core innovation in StreamV2V lies in a feature bank that archives and reuses information from past frames, thereby ensuring temporal consistency without the need for extensive computational resources.
  3. Integration with Diffusion Models:
    • The model seamlessly integrates with existing image diffusion models without requiring additional training or fine-tuning, enhancing its adaptability and efficiency.
  4. User Study and Quantitative Metrics:
    • Extensive user studies and quantitative evaluations, such as the CLIP score and warp error, validate the model’s performance. Specifically, users significantly preferred StreamV2V over StreamDiffusion and CoDeF, with win rates exceeding 70% and 80%, respectively.

Theoretical Implications

StreamV2V extends existing knowledge on diffusion models by incorporating temporal continuity in video processing through a backward-looking principle. This is achieved by maintaining a dynamic feature bank, which consolidates relevant information from past frames. The feature bank's capacity is managed through a dynamic merging strategy, ensuring it remains compact and efficient. This approach mitigates the redundancy and inefficiencies associated with storing all past frames or using sliding window techniques.

Practical Implications

Practically, StreamV2V represents a substantial leap in real-time video processing capabilities. Its ability to handle high-resolution video (512x512) at 20 FPS on a single A100 GPU makes it a viable option for various applications, including real-time webcam video translation and AI-assisted drawing rendering. By eliminating the need for batch processing and extensive frame loading, StreamV2V can be integrated into user-facing applications without significant performance trade-offs.

Technical Details

Extended self-Attention (EA)

The model extends traditional self-attention mechanisms to include stored keys and values from the feature bank, enabling a weighted sum of similar regions across frames. This extension allows for highly detailed and temporally consistent video frame generation.

Feature Fusion (FF)

A complementary approach to EA, feature fusion explicitly merges past frame features based on their cosine similarity. By fusing similar regions, FF enhances the temporal coherence in fine-grained features, further mitigating flickering artifacts.

Dynamic Feature Bank

The feature bank updates dynamically by merging redundant features from incoming and stored frames. This dynamic merging technique ensures the bank remains both compact and informative, crucial for maintaining real-time processing capabilities.

Experimental Results

Quantitative Metrics

  • CLIP Score: StreamV2V achieved a comparable CLIP score to existing state-of-the-art models but stands out in terms of processing speed.
  • Warp Error: An improvement in warp error metrics confirms StreamV2V’s superior temporal consistency.

Runtime Performance

  • Empirical evaluations on a single A100 GPU demonstrate StreamV2V’s remarkable efficiency, processing a four-second 512x512 resolution video with 30 FPS in just nine seconds, significantly faster than contemporaneous methods.

Future Directions

While StreamV2V marks a substantial advancement in V2V translation, certain challenges remain. The model occasionally struggles with significant alterations in object appearances or maintaining consistency under large motions. Future research could explore more advanced feature fusion and attention mechanisms to handle these scenarios. Moreover, integrating more sophisticated image-editing techniques could enhance its ability to handle complex text-guided transformations.

Conclusion

StreamV2V exemplifies a significant stride in the domain of real-time video processing, leveraging diffusion models for temporally consistent video-to-video translation. By addressing the limitations of batch processing through a streaming approach and backward-looking mechanisms, it sets the stage for more responsive and efficient V2V applications. The research presented provides both theoretical insights and practical implications that could inspire further advancements in the field.

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