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ByteDance Unveils Goku to Take on Google’s Luma and OpenAI’s Sora
ByteDance Unveils Goku to Take on Google’s Luma and OpenAI’s Sora
Super Saiyan Hype

ByteDance Unveils Goku to Take on Google’s Luma and OpenAI’s Sora

While the models may be less powerful than the anime character Goku, they do look pretty impressive.

Published Updated

Key takeaways

  • ByteDance launches Goku, a new family of image-video generation models to compete with Google and OpenAI.
  • Goku models leverage advanced techniques like rectified flow and Transformer networks for high-quality video generation.
  • Research shows Goku sets new benchmarks against competitors in qualitative and quantitative video evaluations.
  • Goku's capabilities include creating marketing avatars, product videos, and visualizing poetry.
  • The introduction follows ByteDance's OmniHuman-1, a video AI model that generates videos from images.
5 min

ByteDance, the parent company of TikTok, has dropped a family of joint image-video generation models called Goku. The models seem to be named after the popular anime character ‘Goku’ from the Dragon Ball series. 

This comes right after the company teased a video AI model that generates videos from images, dubbed OmniHuman-1.

Researchers claim that the Goku models help create product videos featuring AI-generated influencers, marketing avatars, landscape demos, visualising Chinese poetry, portrait video demos, and more.

The research paper attributes the model's ability to generate high-quality videos to several key factors. One is the implementation of a rectified flow (RF) formulation for joint image and video generation and the employment of a 3D joint image-video VAE to compress inputs into a shared latent space. 

Moreover, the architecture features a Transformer network with full attention, enhanced with techniques like FlashAttention, sequence parallelism, Patch n’ Pack, 3D RoPE position embedding, and Q-K normalisation.

The paper also states that the Goku models demonstrate superior performance in both qualitative and quantitative evaluations, setting new benchmarks when compared to competitors like Luma, Open-Sora, Mira, and Pika.

Goku achieved 0.76 on GenEval, 83.65 on DPG-Bench for text-to-image generation, and 84.85 on VBench for text-to-video tasks. You can see the benchmark results below. 

(Credit: GitHub page)

“We believe that this work provides valuable insights and practical advancements for the research community in developing joint image-and-video generation models,” the researchers said.

The model’s ability to generate high-quality product videos featuring AI-generated influencers and other realistic visuals could hugely benefit content creators, influencers, marketers, and others.

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