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Shap-E: Generating Conditional 3D Implicit Functions (2305.02463v1)

Published 3 May 2023 in cs.CV and cs.LG

Abstract: We present Shap-E, a conditional generative model for 3D assets. Unlike recent work on 3D generative models which produce a single output representation, Shap-E directly generates the parameters of implicit functions that can be rendered as both textured meshes and neural radiance fields. We train Shap-E in two stages: first, we train an encoder that deterministically maps 3D assets into the parameters of an implicit function; second, we train a conditional diffusion model on outputs of the encoder. When trained on a large dataset of paired 3D and text data, our resulting models are capable of generating complex and diverse 3D assets in a matter of seconds. When compared to Point-E, an explicit generative model over point clouds, Shap-E converges faster and reaches comparable or better sample quality despite modeling a higher-dimensional, multi-representation output space. We release model weights, inference code, and samples at https://github.com/openai/shap-e.

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Authors (2)
  1. Heewoo Jun (14 papers)
  2. Alex Nichol (10 papers)
Citations (248)
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Summary

Overview of "Shap$: Generating Conditional 3D Implicit Functions&quot;</h2> <p>The paper introduces Shap$, a generative model designed to produce 3D assets by generating parameters for implicit functions. Unlike other 3D generative models that rely on single output representations, Shap$ efficiently leverages the ability to render both textured meshes and neural radiance fields (NeRFs), thereby offering flexibility in its application. This model is trained on a paired dataset of 3D and textual data, optimizing the generation of intricate and varied 3D assets.</p> <h3 class='paper-heading'>Methodology</h3> <p>The model is developed using a two-stage training process:</p> <ol> <li><strong>Encoder Training</strong>: Initially, an encoder is trained to deterministically map 3D assets into parameters of an implicit function using a Transformer-based architecture. The encoded data directly translate into parameters of the multilayer perceptron (MLP), which serves as the core of the NeRF and Signed Texture Fields (STF).</li> <li><strong>Conditional Diffusion Model</strong>: Building upon the encoder&#39;s outputs, a conditional diffusion model is trained to capture the nuanced dynamics encapsulated in the latent representations. The diffusion process maintains high-dimensional, multi-representation output capabilities, thereby enabling Shap$ to achieve faster convergence while maintaining superior or comparable quality.

Key Findings

  • Performance: ShapdemonstratesacceleratedconvergenceandimprovedsamplequalitywhenbenchmarkedagainstPointdemonstrates accelerated convergence and improved sample quality when benchmarked against Point, a recent explicit 3D generative model. This efficacy is attained despite managing a higher-dimensional output space.
  • Diversity and Complexity: The model excels at generating a diverse array of complex 3D structures within seconds, an advantage attributed to the robust conditional attributes it encodes.
  • Evaluation: The authors showcase Shap$’s strengths using CLIP R-Precision metrics, where it meets or exceeds the performance of comparable models. Furthermore, the model&#39;s ability to render both NeRFs and textured meshes simultaneously presents a substantial advantage in various applications.</li> </ul> <h3 class='paper-heading'>Implications and Future Directions</h3> <p>Practically, the ability of Shap$ to render through both NeRFs and textured mesh compressions opens up numerous possibilities in virtual reality, gaming, and digital content creation industries. Theoretical advancements provided by this approach highlight the potential of INRs in addressing challenges related to fixed-size grid representations typical in 3D modeling. It bridges the gap by offering resolution-independent, end-to-end differentiable solutions.

    Future research avenues might explore scaling the dataset and fine-tuning the encoder for even more complex asset generation, including improved texture and fine detail capture. Additionally, integrating image-based objective functions to guide the sampling process could further enhance the model's practical utility. Possible investigation into reducing computation time while sustaining high quality presents an ongoing challenge in the field of implicit function modeling.

    Conclusion

    Shapprovidesnotablecontributionstothegenerativemodelingof3Dassets.BymovingbeyondexplicitmodelslikePointprovides notable contributions to the generative modeling of 3D assets. By moving beyond explicit models like Point, Shap$ establishes a new benchmark by coupling flexibility with efficiency. The paper effectively demonstrates that embracing implicit representations can be advantageous, paving the way for future enhancements and applications in AI-driven 3D modeling.

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GitHub

  1. GitHub - openai/shap-e: Generate 3D objects conditioned on text or images (11,390 stars)
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