StreamV2V

Preview：

Introduce：

StreamV2V is a diffusion model that enables real-time video-to-video (V2V) translation via user prompts. Different from the traditional batch processing method, StreamV2V uses streaming processing and can process unlimited frames of video. At its core, it maintains a signature library that stores information about past frames. For new frames, StreamV2V uses extended self-attention and direct feature fusion techniques to fuse similar past features directly into the output. The signature database is constantly updated by merging stored and new features, keeping it compact and informative. StreamV2V stands out for its adaptability and efficiency, seamlessly integrating with image diffusion models without fine-tuning.

Stakeholders:
StreamV2V is suitable for professionals and researchers who need real-time video processing and translation. It is particularly suitable for areas such as video editing, film post-production, real-time video augmenting, and virtual reality because of its ability to provide fast, seamless video processing while maintaining high quality output.
Usage Scenario Examples:

• Video editors use StreamV2V to adjust video styles and effects in real time.
• Film post-production teams use StreamV2V for real-time preview and adjustment of special effects.
• Virtual reality developers use StreamV2V to provide dynamic tuning of live video content for VR experiences.

The features of the tool:

• Real-time video to video translation: Supports unlimited frame video processing.
• User Tip: Allow the user to enter instructions to guide the video translation.
• Signature database maintenance: Stores the intermediate converter features of past frames.
• Extended Self-attention (EA) : Connects stored keys and values directly to the self-attention calculation of the current frame.
• Direct feature fusion (FF) : Similar features in the bank are retrieved by cosine similarity matrix and weighted summation fusion is performed.
• High efficiency: Running at 20 FPS on a single A100 GPU, 15, 46, 108 and 158 times faster than FlowVid, CoDeF, Rerender and TokenFlow.
• Excellent time consistency: confirmed by quantitative indicators and user studies.

Steps for Use:

• Step 1: Visit StreamV2V’s official website.
• Step 2: Read about the introduction and functionality of the model.
• Step 3: Set user prompts as needed to guide the direction of video translation.
• Step 4: Upload or connect the video source that needs to be translated.
• Step 5: Launch the StreamV2V model to start real-time video translation.
• Step 6: Observe the video output during translation and adjust the parameters as needed.
• Step 7: After the translation is complete, download or use the translated video content directly.

Tool’s Tabs: Video translation, diffusion model