3D Scene Rendering

Text2Light: Unleash Immersive Realism through Text-Driven Photorealistic 3D Scene Rendering

Synopsis

Text2Light enables photorealistic HDR 3D scene rendering from text, boosting digital experiences in gaming, education, virtual tourism and metaverse development. Its AI-driven technology surpasses current methods, offering high-quality, user-friendly HDR panorama generation.


Opportunity 

Embark on a journey into the heart of immersive digital experiences with this state-of-the-art AI technology, capable of generating photorealistic rendering of HDR 3D scenes. Given a text-driven scene description as input, Text2Light generates a high-quality HDR panorama.  

As the metaverse and virtual reality reshape the way we interact with the digital world, the demand for such AI-driven HDR 3D scene generation has never been greater. Whether it's exploring breathtaking virtual landscapes or designing the next iconic virtual destination, this free-form text photorealistic rendering technology sets the standard for what's possible. High-quality high dynamic range images (HDRIs), particularly HDR panoramas, are crucial in creating photorealistic lighting and 360-degree reflections for 3D scenes in computer graphics. These HDRIs are in high demand due to their ability to imbue scenes with stunning realism. However, the process of capturing HDRIs is notoriously challenging, underscoring the need for a versatile and user-friendly generative model that allows even non-experts to intuitively control the generation process. Despite significant advancements in the field, existing state-of-the-art methods continue to grapple with the synthesis of high-quality panoramas, especially for complex scenes.  


Technology 

Text2Light generates photorealistic rendering of HDR 3D scenes. This zero-shot text-driven framework empowers users to generate HDRIs with resolutions exceeding 4K, all without the need for paired training data. Text2Light interprets a free-form textual description of the scene provided by the user, synthesising a corresponding panorama in a low dynamic range (LDR) and low resolution format. This step serves as the foundation upon which the subsequent enhancements are built. Following panorama generation, Text2Light employs super-resolution and inverse tone mapping to elevate the LDR panorama in both resolution and dynamic range, resulting in a final HDR panorama of exceptional quality.

 

Figure 1: Examples of Text2Light in generating HDRIs. The generated results can be directly used to render 3D scenes.

Figure 1: Examples of Text2Light in generating HDRIs. The generated results can be directly used to render 3D scenes.
Figure 1: Examples of Text2Light in generating HDRIs. The generated results can be directly used to render 3D scenes. 

 

Applications & Advantages  

Main application areas: 

  1. Gaming and entertainment: Elevate gaming experiences with breathtaking virtual worlds, captivating audiences with immersive storytelling. 

  2. Education and training: Create realistic training environments for various industries, from healthcare to aviation, improving skills and decision-making. 

  3. Virtual tourism: Offer travellers immersive previews of destinations, revolutionising the travel and tourism industry. 

  4. Metaverse development: Fuel the growth of the metaverse by providing the foundation for lifelike virtual experiences 

Advantages: 

  • High-quality HDR panorama generation from text descriptions 

  • Zero-shot framework, requiring no paired training data 

  • Resolution exceeding 4K, with super-resolution and inverse tone mapping 

  • User-friendly, allowing non-experts to intuitively control the generation process 

  • Enhances immersive realism in digital experiences


Reference 

  1. https://frozenburning.github.io/projects/text2light/ 

  2. Zhaoxi Chen, Guangcong Wang and Ziwei Liu, Text2Lgiht: Zero-Shot text-Driven HDR Panorama Generation, ACM Transactions on Graphics (TOG), Volume 41, Issue 6, article no.: 195, pages 1-16, 2022, ACM New York, NY, USA, DOI: 10.1145/3550454.3555447, https://arxiv.org/abs/2209.09898.pdf 

 

 

Inventor

Prof LIU Ziwei