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LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control
Portrait Animation aims to synthesize a lifelike video from a single source image, using it as an appearance reference, with motion (i.e., facial expressions and head pose) derived from a driving video, audio, text, or generation. Instead of following mainstream diffusion-based methods, we explore and extend the potential of the implicit-keypoint-based framework, which effectively balances computational efficiency and controllability. Building upon this, we develop a video-driven portrait animation framework named LivePortrait with a focus on better generalization, controllability, and efficiency for practical usage. To enhance the generation quality and generalization ability, we scale up the training data to about 69 million high-quality frames, adopt a mixed image-video training strategy, upgrade the network architecture, and design better motion transformation and optimization objectives. Additionally, we discover that compact implicit keypoints can effectively represent a kind of blendshapes and meticulously propose a stitching and two retargeting modules, which utilize a small MLP with negligible computational overhead, to enhance the controllability.
page: https://liveportrait.github.io/
paper: https://arxiv.org/abs/2407.03168
code: https://github.com/KwaiVGI/LivePortrait
jupyter: https://github.com/camenduru/LivePortrait-jupyter
https://news.1rj.ru/str/DataScienceT⭐️
Portrait Animation aims to synthesize a lifelike video from a single source image, using it as an appearance reference, with motion (i.e., facial expressions and head pose) derived from a driving video, audio, text, or generation. Instead of following mainstream diffusion-based methods, we explore and extend the potential of the implicit-keypoint-based framework, which effectively balances computational efficiency and controllability. Building upon this, we develop a video-driven portrait animation framework named LivePortrait with a focus on better generalization, controllability, and efficiency for practical usage. To enhance the generation quality and generalization ability, we scale up the training data to about 69 million high-quality frames, adopt a mixed image-video training strategy, upgrade the network architecture, and design better motion transformation and optimization objectives. Additionally, we discover that compact implicit keypoints can effectively represent a kind of blendshapes and meticulously propose a stitching and two retargeting modules, which utilize a small MLP with negligible computational overhead, to enhance the controllability.
page: https://liveportrait.github.io/
paper: https://arxiv.org/abs/2407.03168
code: https://github.com/KwaiVGI/LivePortrait
jupyter: https://github.com/camenduru/LivePortrait-jupyter
https://news.1rj.ru/str/DataScienceT
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Forwarded from 🐳
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A powerful 1,108-page book that details the implementation of ML and Deep Learning algorithms using PyTorch, NumPy/MXNet, JAX, and TensorFlow.
Lectures based on this book are given at 500 universities in 70 countries.
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Forwarded from Nagmani
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https://news.1rj.ru/str/DataScienceT
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🚀 Dataset: https://paperswithcode.com/dataset/behave
https://news.1rj.ru/str/DataScienceT
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Forwarded from Machine Learning with Python
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⚡️ EMO-Disentanger
🖥 Github: https://github.com/yuer867/emo-disentanger
📕 Paper: https://arxiv.org/abs/2407.20955v1
🚀 Dataset: https://paperswithcode.com/dataset/emopia
https://news.1rj.ru/str/DataScienceT⭐️
🚀 Dataset: https://paperswithcode.com/dataset/emopia
https://news.1rj.ru/str/DataScienceT
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👨🏻💻 Have you ever thought about what makes artificial intelligence projects and machine learning models work better and smarter? The answer is simple: quality data!
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Forwarded from Machine Learning with Python
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Dear subscriber, we would like to thank you very much for supporting our channel, and as a token of our gratitude we would like to provide you with free access to Lisa's investor channel, with the help of which you can earn today
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Be sure to take advantage of our gift, admission is free, don't miss the opportunity, change your life for the better.
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No learning rates needed: Introducing SALSA - Stable Armijo Line Search Adaptation
💻 Github: https://github.com/themody/no-learning-rates-needed-introducing-salsa-stable-armijo-line-search-adaptation
📕 Paper: https://arxiv.org/abs/2407.20650v1
✈️ Dataset: https://paperswithcode.com/dataset/cifar-10
🌐 https://news.1rj.ru/str/DataScienceT ⭐️
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VADER is a method for aligning the results of diffusion models for video generation;
VADER allows to improve various models such as VideoCrafter, OpenSora, ModelScope and StableVideoDiffusion using different approaches such as HPS, PickScore, VideoMAE, VJEPA, YOLO, Aesthetics and others.
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DeepInteraction & DeepInteraction++
🖥 Github: https://github.com/fudan-zvg/deepinteraction
📕 Paper: https://arxiv.org/abs/2408.05075v1
🚀 Dataset: https://paperswithcode.com/dataset/nuscenes
🚀 Dataset: https://paperswithcode.com/dataset/nuscenes
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Paper Name: The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery
Paper: https://arxiv.org/pdf/2408.06292v2.pdf
Code: https://github.com/sakanaai/ai-scientist
🌐 https://news.1rj.ru/str/DataScienceT ⭐️
Paper: https://arxiv.org/pdf/2408.06292v2.pdf
Code: https://github.com/sakanaai/ai-scientist
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