Tangia - Interactive Streaming

Supercharge chat engagement on your stream with Tangia

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Tangia - Interactive Streaming

Введение

What is Tangia?

Tangia is an interactive streaming platform that offers a wide range of AI technologies for various purposes, such as custom TTS, AI conversations, and image generation.

How can I use Tangia?

Users can make use of Tangia's functionalities, like custom TTS, AI conversations, and image generation, to enrich their streams and interact with their audience. Tangia helps create a more engaging and entertaining streaming experience.

Features of Tangia

Custom TTS

  • Utilize Tangia TTS Studio to transform your voice into a highly realistic TTS in just minutes
  • Instantly access over 150 hand-crafted TTS voices for your stream

AI Conversations

  • Generate spontaneous moments on stream with AI conversations
  • Interact with AI characters, discussing topics ranging from the meaning of life to teasing your streamer for losing again

Image Generation

  • Translate chat's ideas into images using image generation interactions
  • Allow chat to suggest prompts, and Tangia will create an image live on stream

Soundbites

  • Enable chat to submit clips from your stream for conversion into playable soundbite interactions
  • Quickly approve and incorporate soundbites into your stream

Alerts and Media Share

  • Create alerts with Tangia to trigger TTS messages, interactions, videos, or any other Tangia feature
  • Focus on media sharing with support for various platforms, moderator access, and a built-in trimmer

TangiaPass and Affiliate & Partner Status

  • Enhance your Tangia experience with TangiaPass, offering subscriber discounts, additional custom TTS slots, and more
  • Earn REP by sharing interactions with the community
  • Gain early access to features and events as an Affiliate or Partner

Tangia Parties

  • Establish a Bit/Token goal that initiates a dance party once achieved
  • Engage your community through Tangia Parties

Tangia - Альтернатива

Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold

Synthesizing visual content that meets users' needs often requires flexible and precise controllability of the pose, shape, expression, and layout of the generated objects. Existing approaches gain controllability of generative adversarial networks (GANs) via manually annotated training data or a prior 3D model, which often lack flexibility, precision, and generality. In this work, we study a powerful yet much less explored way of controlling GANs, that is, to

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