NCA-GENM Exam Dumps | You're designing a generative A1 system to create realistic 3D models of furniture from text descriptions.

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Question 74/192

You're designing a generative A1 system to create realistic 3D models of furniture from text descriptions. Which of the following approaches would likely yield the MOST realistic and detailed results, and how can NVIDIA's tools contribute to its success?

A. Using a simple variational autoencoder (VAE) trained on a dataset of 3D furniture models, without any text-based guidance. NVIDIA's GPUs can accelerate the VAE training process.

B. Employing a text-to-image model like Stable Diffusion to generate 2D images of the furniture from different viewpoints, and then using multi-view stereo reconstruction to create a 3D model. NVIDIA's GPUs can accelerate both the image generation and reconstruction processes.

C. Training a generative adversarial network (GAN) to directly generate 3D meshes from text descriptions, using a differentiable renderer as part of the discriminator. NVIDIA's GPUs are essential for training GANs with differentiable renderers.

D. Using a rule-based system to procedurally generate 3D furniture models based on keywords extracted from the text descriptions. NVIDIA's PhysX engine can be used to simulate realistic physics interactions.

E. None of the above

Correct Answer: C

Directly generating 3D meshes from text using a GAN with a differentiable renderer (C) allows the model to learn complex relationships between text and 3D geometry. Differentiable rendering enables the discriminator to evaluate the realism of the generated 3D models. VAEs (A) are less capable of generating high-detail models. Multi-view stereo (B) can be effective, but relies on the quality of the 2D images. Rule- based systems (D) lack the flexibility to capture the nuances of natural language. NVIDIA GPIJs are crucial for the computationally intensive GAN training and differentiable rendering processes. GAN's are difficult to train. The best option would be to directly train them on NVIDIA GPU and a Differentiable renderer.

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