Showing 4 open source projects for "image sequence"

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    HunyuanVideo-I2V

    HunyuanVideo-I2V

    A Customizable Image-to-Video Model based on HunyuanVideo

    HunyuanVideo-I2V is a customizable image-to-video generation framework from Tencent Hunyuan, built on their HunyuanVideo foundation. It extends video generation so that given a static reference image plus an optional prompt, it generates a video sequence that preserves the reference image’s identity (especially in the first frame) and allows stylized effects via LoRA adapters.
    Downloads: 2 This Week
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  • 2
    HunyuanVideo-I2V

    HunyuanVideo-I2V

    A Customizable Image-to-Video Model based on HunyuanVideo

    HunyuanVideo-I2V is a customizable image-to-video generation framework developed by Tencent, extending the capabilities of HunyuanVideo. It allows for high-quality video creation from still images, using PyTorch and providing pre-trained model weights, inference code, and customizable training options. The system includes a LoRA training code for adding special effects and enhancing video realism, aiming to offer versatile and scalable solutions for generating videos from static image inputs.
    Downloads: 2 This Week
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  • 3
    Amiga Memories

    Amiga Memories

    A walk along memory lane

    ...The purpose of the script is to define the spoken and written content. The spoken text will be read by a voice synthesizer (Text To Speech or TTS), the written text is simply drawn on the image as subtitles. Here, in addition to the spoken & written narration, the script controls the camera movements as well as the LED activity of the computer. Amiga Memories' video images are computed by the GameStart 3D engine (pre-HARFANG 3D). Although the 3D assets are designed to be played back in real-time with a variable framerate, the engine is capable of breaking down the video sequence into the 30th or 60th of a second, as TGA files.
    Downloads: 0 This Week
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  • 4
    DCVGAN

    DCVGAN

    DCVGAN: Depth Conditional Video Generation, ICIP 2019.

    This paper proposes a new GAN architecture for video generation with depth videos and color videos. The proposed model explicitly uses the information of depth in a video sequence as additional information for a GAN-based video generation scheme to make the model understands scene dynamics more accurately. The model uses pairs of color video and depth video for training and generates a video using the two steps. Generate the depth video to model the scene dynamics based on the geometrical information. To add appropriate color to the geometrical information of the scene, the domain translation from depth to color is performed for each image. ...
    Downloads: 0 This Week
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