CMU Researchers Propose Pix2pix3D: A 3D-Aware Conditional Generative Model For Controllable Photorealistic Image Synthesis

Are you looking for a way to generate 3D material with user-controllable picture and video synthesis? If so, you’ve come to the right place. In this blog post, we’ll discuss a new approach to conditional image synthesis that is 3D-aware and enables the creation and manipulation of 3D material from various angles. Read on to learn more about this groundbreaking research.

The Challenge of Generating 3D Content

Generating 3D content from 2D user inputs can be a difficult task. Obtaining large datasets with coupled user inputs and intended 3D outputs is expensive and time-consuming. Additionally, users may desire to describe the specifics of 3D objects using 2D interfaces from various angles, but this can lead to multi-view user inputs that are not 3D-consistent, resulting in contradictory signals for the production of 3D content.

Introducing 3D Neural Scene Representations

To overcome these issues, researchers have applied 3D neural scene representations to conditional generative models. This approach contains semantic information in 3D to facilitate cross-view editing and can be presented as 2D label maps from various angles. With a pixel-aligned conditional discriminator, the appearance and labels look realistic when rendered into new views. Additionally, a reconstruction loss assures the alignment between 2D user inputs and matching 3D material.

The Results

This approach was tested on the CelebAMask-HQ, AFHQ-cat, and shapenetcar datasets for 3D-aware semantic picture synthesis. The results showed that this approach effectively uses different 2D user inputs, such as segmentation maps and edge maps. It also surpassed several 2D and 3D baselines, including SEAN, SofGAN, and Pix2NeRF versions. Moreover, researchers minimized the effects of different design decisions and showed how this methodology could be used in applications like cross-view editing and explicit user control over semantics and style.


In conclusion, this approach to conditional image synthesis is 3D-aware and enables the creation and manipulation of 3D material from various angles. It effectively uses different 2D user inputs and surpasses several 2D and 3D baselines. To view further findings and code, visit their website.

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