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Avatar Video Clips Download
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Syreeta Emmons
Jan 26, 2024, 2:49:28 AMJan 26
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Text to speech avatar is currently in public preview. This preview is provided without a service-level agreement, and is not recommended for production workloads. Certain features might not be supported or might have constrained capabilities. For more information, see Supplemental Terms of Use for Microsoft Azure Previews.
Custom text to speech avatar model building requires training on a video recording of a real human speaking. This person is the avatar talent. You must get sufficient consent under all relevant laws and regulations from the avatar talent to create a custom avatar from their talent's image or likeness.
avatar video clips download
Download File https://lomogd.com/2xw9Hb
The custom text to speech avatar doesn't support customization of clothes or looks. Therefore, it's essential to carefully design and prepare the avatar's appearance when recording the training data. Consider the following tips:
Modern deep learning-based video portrait generators render synthetic talking-head videos with impressive levels of photorealism, ushering in new user experiences such as videoconferencing with limited-bandwidth connectivity. Their safe adoption, however, requires a mechanism to verify if the rendered video is trustworthy. For instance, in videoconferencing we must identify cases when a synthetic video portrait uses the appearance of an individual without their consent. We term this task ''avatar fingerprinting''. We propose to tackle it by leveraging the observation that each person emotes in unique ways and has characteristic facial motion signatures. These signatures can be directly linked to the person ''driving'' a synthetic talking-head video. We learn an embedding in which the motion signatures derived from videos driven by one individual are clustered together, and pushed away from those of others, regardless of the facial appearance in the synthetic video. This embedding can serve as a tool to help verify authorized use of a synthetic talking-head video. Avatar fingerprinting algorithms will be critical as talking head generators become more ubiquitous, and yet no large scale datasets exist for this new task. Therefore, we contribute a large dataset of people delivering scripted and improvised short monologues, accompanied by synthetic videos in which we render videos of one person using the facial appearance of another. Since our dataset contains human subjects' facial data, we have taken many steps to ensure proper use and governance, including: IRB approval, informed consent prior to data capture, removing subject identity information, pre-specifying the subject matter that can be discussed in the videos, allowing subjects the freedom to revoke our access to their provided data at any point in future (and stipulating that interested third parties maintain current contact information with us so we can convey these changes to them). Lastly, we acknowledge the societal importance of introducing guardrails for the use of talking-head generation technology, and note that we present this work as a step towards trustworthy use of such technologies.
Our dataset also contains a synthetic component (Figure 3): talking-head videos generated using three facial reenactment methods: 1) face-vid2vid, 2) LIA(currently released for test-set only), and 3) TPS (currently released for test-set only). To generate these synthetic videos, we pool the data of 46 subjects from our video call-based data capture with those from the RAVDESS and CREMA-D datasets, leading to a total of 161 unique identities in the synthetic part of our dataset. For each synthetic talking-head video, a neutral facial image of an identity is driven by expressions from their own videos -- termed ''self-reenactment'' -- and the videos of all other identities -- termed ''cross-reenactment''. In a self-reenactment, the identity shown in the synthetic video (''target'' identity) matches the identity driving the video (''driving'' identity). In contrast, the target and driving identities are not the same in a cross-reenactment -- a case which could potentially indicate unauthorized use of the target identity. By recording scripted and free-form monologues for the original data of the 46 subjects, and providing synthetically-generated self and cross-reenactments for all identities (including those from RAVDESS and CREMA-D), we combine many unique properties in our dataset. This makes our dataset well-suited for training and evaluating avatar fingerprinting models and it can also be beneficial for other related tasks such as detection of synthetically-generated content.
We also propose a baseline method for avatar fingerprinting. Given an input clip, we rely on per-frame facial landmarks to derive the input features to a temporal-processing convolutional network. Here, we are only showing a subset of the 126 landmarks that we use. To abstract away from underlying shape while still capturing facial dynamics, we compute normalized pairwise landmark distances, which are concatenated across frames to obtain the final input feature for the network. We want to cluster these features based on the driving identity, regardless of the target identity. To achieve the desired clustering we propose a dynamic identity embedding contrastive loss. With our loss formulation, a clip driven by a given identity (say ID1 in the case below) would be pulled towards other clips driven by the same identity, while being pushed away from the clips driven by another identity (say ID4 in the case below).
Here we evaluate whether our method predicts embedding vectors that lie close together when the synthetic video clips have the same driving identity. We first choose a reference identity: for example, ID1 in the first case shown above. The two leftmost videos for this case show the synthetic video clips driven by ID1. The two rightmost clips have ID1 as the target for other drivers. We report the average Euclidean distance, d, between the dynamic facial identity embedding vectors of each of these four clips and a set of reference clips containing self-reenactments of ID1. As is clear from the values of d, when a clip is driven by ID1, it lies closer to the other videos driven by ID1. On the other hand, when ID1 is a target for other drivers, their embedding vectors are far from those of the clips driven by ID1. A similar observation holds for ID5, and ID7. We analyze this further in the paper with additional AUC-based metrics.
AI Studios from DeepBrain AI is a competitor of Synthesia for text-to-video and/or audio-to-video content generation. It has been particularly well received for its speech and audio quality, giving users the ability to easily mix audio and adjust tones and accents for AI avatars that better reflect what sounds natural to their audience.
In financial services in particular, AI Studios and its avatars have been used by major enterprises to create virtual finance analysts for personalized videos and virtual lobby assistants for bank kiosks in South Korea.
InVideo is a video-making platform with AI features that support everything from script generation and avatar generation to slideshow design and YouTube video editing. Its template library is one of the most extensive in the market, covering topics and format types for advertising, slideshows, memes, YouTube, Instagram, music videos, breaking news, and logo videos.
Pictory is another AI video generation platform that is best suited for content marketing and social media video projects. It is a particularly effective solution for creating micro-content, or shorter clips and highlight reels from existing long-form content.
Many businesses do not have the budgets to pay actors or employees to act as talking heads for brand videos, but they nonetheless want the personal touch of an onscreen personality. AI avatars can be developed to match different appearances, genders, and other expectations, with personalities, tones, accents, and other unique features added to synthetic voices.
The best AI avatar solutions create natural-looking avatars and give users the ability to custom-create their own avatars. These avatars can be used for personalized sales and marketing videos, e-learning and training videos, and other forms of media that benefit from a friendly face.
Although many other AI video generation tools exist for casual mobile users and use cases, we mostly steered away from those tools in favor of platforms that offer enterprise features such as video embeds and exports, useful integrations, business templates, advanced AI avatars and audio synthesis, and other features that support an enterprise video-making workflow.
While compiling our list, we searched for tools that supported high-resolution video uploads and downloads and audio mixing and synchronization capabilities. We also looked for tools that received favorable customer reviews for AI avatar and sound quality performance and that include basic transition, animation, and intro and outro functionalities.
With the help of an AI video tool, users can now take on many of the most complicated video tasks, ranging from editing footage and audio to creating shorter clips and summaries from long-form content.
This workflow uses Sieve building blocks (sieve.reference), so we can skip all the boilerplate code and the nuances with each model, empowering us to build features like avatar generation fast. If you want to dive deeper into the implementation of each of these models, check out our examples repo.
To integrate your own AI avatar videos into your app in a few minutes, you can follow the guide above or clone our template from the UI and access any of your job outputs via our API. We offer $20 of credit, so you can get started right away.
Another great feature of Pictory is that you can create shareable video highlight reels, which proves useful for those looking to create trailers or share short clips on social media. Besides these great features, you can also automatically caption your videos and automatically summarize long videos.
f5d0e4f075
Custom text to speech avatar model building requires training on a video recording of a real human speaking. This person is the avatar talent. You must get sufficient consent under all relevant laws and regulations from the avatar talent to create a custom avatar from their talent's image or likeness.
avatar video clips download
Download File https://lomogd.com/2xw9Hb
The custom text to speech avatar doesn't support customization of clothes or looks. Therefore, it's essential to carefully design and prepare the avatar's appearance when recording the training data. Consider the following tips:
Modern deep learning-based video portrait generators render synthetic talking-head videos with impressive levels of photorealism, ushering in new user experiences such as videoconferencing with limited-bandwidth connectivity. Their safe adoption, however, requires a mechanism to verify if the rendered video is trustworthy. For instance, in videoconferencing we must identify cases when a synthetic video portrait uses the appearance of an individual without their consent. We term this task ''avatar fingerprinting''. We propose to tackle it by leveraging the observation that each person emotes in unique ways and has characteristic facial motion signatures. These signatures can be directly linked to the person ''driving'' a synthetic talking-head video. We learn an embedding in which the motion signatures derived from videos driven by one individual are clustered together, and pushed away from those of others, regardless of the facial appearance in the synthetic video. This embedding can serve as a tool to help verify authorized use of a synthetic talking-head video. Avatar fingerprinting algorithms will be critical as talking head generators become more ubiquitous, and yet no large scale datasets exist for this new task. Therefore, we contribute a large dataset of people delivering scripted and improvised short monologues, accompanied by synthetic videos in which we render videos of one person using the facial appearance of another. Since our dataset contains human subjects' facial data, we have taken many steps to ensure proper use and governance, including: IRB approval, informed consent prior to data capture, removing subject identity information, pre-specifying the subject matter that can be discussed in the videos, allowing subjects the freedom to revoke our access to their provided data at any point in future (and stipulating that interested third parties maintain current contact information with us so we can convey these changes to them). Lastly, we acknowledge the societal importance of introducing guardrails for the use of talking-head generation technology, and note that we present this work as a step towards trustworthy use of such technologies.
Our dataset also contains a synthetic component (Figure 3): talking-head videos generated using three facial reenactment methods: 1) face-vid2vid, 2) LIA(currently released for test-set only), and 3) TPS (currently released for test-set only). To generate these synthetic videos, we pool the data of 46 subjects from our video call-based data capture with those from the RAVDESS and CREMA-D datasets, leading to a total of 161 unique identities in the synthetic part of our dataset. For each synthetic talking-head video, a neutral facial image of an identity is driven by expressions from their own videos -- termed ''self-reenactment'' -- and the videos of all other identities -- termed ''cross-reenactment''. In a self-reenactment, the identity shown in the synthetic video (''target'' identity) matches the identity driving the video (''driving'' identity). In contrast, the target and driving identities are not the same in a cross-reenactment -- a case which could potentially indicate unauthorized use of the target identity. By recording scripted and free-form monologues for the original data of the 46 subjects, and providing synthetically-generated self and cross-reenactments for all identities (including those from RAVDESS and CREMA-D), we combine many unique properties in our dataset. This makes our dataset well-suited for training and evaluating avatar fingerprinting models and it can also be beneficial for other related tasks such as detection of synthetically-generated content.
We also propose a baseline method for avatar fingerprinting. Given an input clip, we rely on per-frame facial landmarks to derive the input features to a temporal-processing convolutional network. Here, we are only showing a subset of the 126 landmarks that we use. To abstract away from underlying shape while still capturing facial dynamics, we compute normalized pairwise landmark distances, which are concatenated across frames to obtain the final input feature for the network. We want to cluster these features based on the driving identity, regardless of the target identity. To achieve the desired clustering we propose a dynamic identity embedding contrastive loss. With our loss formulation, a clip driven by a given identity (say ID1 in the case below) would be pulled towards other clips driven by the same identity, while being pushed away from the clips driven by another identity (say ID4 in the case below).
Here we evaluate whether our method predicts embedding vectors that lie close together when the synthetic video clips have the same driving identity. We first choose a reference identity: for example, ID1 in the first case shown above. The two leftmost videos for this case show the synthetic video clips driven by ID1. The two rightmost clips have ID1 as the target for other drivers. We report the average Euclidean distance, d, between the dynamic facial identity embedding vectors of each of these four clips and a set of reference clips containing self-reenactments of ID1. As is clear from the values of d, when a clip is driven by ID1, it lies closer to the other videos driven by ID1. On the other hand, when ID1 is a target for other drivers, their embedding vectors are far from those of the clips driven by ID1. A similar observation holds for ID5, and ID7. We analyze this further in the paper with additional AUC-based metrics.
AI Studios from DeepBrain AI is a competitor of Synthesia for text-to-video and/or audio-to-video content generation. It has been particularly well received for its speech and audio quality, giving users the ability to easily mix audio and adjust tones and accents for AI avatars that better reflect what sounds natural to their audience.
In financial services in particular, AI Studios and its avatars have been used by major enterprises to create virtual finance analysts for personalized videos and virtual lobby assistants for bank kiosks in South Korea.
InVideo is a video-making platform with AI features that support everything from script generation and avatar generation to slideshow design and YouTube video editing. Its template library is one of the most extensive in the market, covering topics and format types for advertising, slideshows, memes, YouTube, Instagram, music videos, breaking news, and logo videos.
Pictory is another AI video generation platform that is best suited for content marketing and social media video projects. It is a particularly effective solution for creating micro-content, or shorter clips and highlight reels from existing long-form content.
Many businesses do not have the budgets to pay actors or employees to act as talking heads for brand videos, but they nonetheless want the personal touch of an onscreen personality. AI avatars can be developed to match different appearances, genders, and other expectations, with personalities, tones, accents, and other unique features added to synthetic voices.
The best AI avatar solutions create natural-looking avatars and give users the ability to custom-create their own avatars. These avatars can be used for personalized sales and marketing videos, e-learning and training videos, and other forms of media that benefit from a friendly face.
Although many other AI video generation tools exist for casual mobile users and use cases, we mostly steered away from those tools in favor of platforms that offer enterprise features such as video embeds and exports, useful integrations, business templates, advanced AI avatars and audio synthesis, and other features that support an enterprise video-making workflow.
While compiling our list, we searched for tools that supported high-resolution video uploads and downloads and audio mixing and synchronization capabilities. We also looked for tools that received favorable customer reviews for AI avatar and sound quality performance and that include basic transition, animation, and intro and outro functionalities.
With the help of an AI video tool, users can now take on many of the most complicated video tasks, ranging from editing footage and audio to creating shorter clips and summaries from long-form content.
This workflow uses Sieve building blocks (sieve.reference), so we can skip all the boilerplate code and the nuances with each model, empowering us to build features like avatar generation fast. If you want to dive deeper into the implementation of each of these models, check out our examples repo.
To integrate your own AI avatar videos into your app in a few minutes, you can follow the guide above or clone our template from the UI and access any of your job outputs via our API. We offer $20 of credit, so you can get started right away.
Another great feature of Pictory is that you can create shareable video highlight reels, which proves useful for those looking to create trailers or share short clips on social media. Besides these great features, you can also automatically caption your videos and automatically summarize long videos.
f5d0e4f075
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