Papers
Topics
Authors
Recent
Search
2000 character limit reached

Progressive3D: Progressively Local Editing for Text-to-3D Content Creation with Complex Semantic Prompts

Published 18 Oct 2023 in cs.CV | (2310.11784v2)

Abstract: Recent text-to-3D generation methods achieve impressive 3D content creation capacity thanks to the advances in image diffusion models and optimizing strategies. However, current methods struggle to generate correct 3D content for a complex prompt in semantics, i.e., a prompt describing multiple interacted objects binding with different attributes. In this work, we propose a general framework named Progressive3D, which decomposes the entire generation into a series of locally progressive editing steps to create precise 3D content for complex prompts, and we constrain the content change to only occur in regions determined by user-defined region prompts in each editing step. Furthermore, we propose an overlapped semantic component suppression technique to encourage the optimization process to focus more on the semantic differences between prompts. Extensive experiments demonstrate that the proposed Progressive3D framework generates precise 3D content for prompts with complex semantics and is general for various text-to-3D methods driven by different 3D representations.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (41)
  1. Re-imagine the negative prompt algorithm: Transform 2d diffusion into 3d, alleviate janus problem and beyond. arXiv preprint arXiv:2304.04968, 2023.
  2. Instructpix2pix: Learning to follow image editing instructions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.  18392–18402, 2023.
  3. Segment anything in 3d with nerfs. arXiv preprint arXiv:2304.12308, 2023.
  4. Fantasia3d: Disentangling geometry and appearance for high-quality text-to-3d content creation. arXiv preprint arXiv:2303.13873, 2023.
  5. Panoptic compositional feature field for editable scene rendering with network-inferred labels via metric learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.  4947–4957, 2023.
  6. Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality, 2023. URL https://lmsys.org/blog/2023-03-30-vicuna/.
  7. DeepFloyd-Team. Deepfloyd-if, 2023. URL https://huggingface.co/DeepFloyd/IF-I-XL-v1.0.
  8. Training-free structured diffusion guidance for compositional text-to-image synthesis. arXiv preprint arXiv:2212.05032, 2022.
  9. threestudio: A unified framework for 3d content generation, 2023. URL https://github.com/threestudio-project/threestudio.
  10. Instruct-nerf2nerf: Editing 3d scenes with instructions. arXiv preprint arXiv:2303.12789, 2023.
  11. Classifier-free diffusion guidance. arXiv preprint arXiv:2207.12598, 2022.
  12. Denoising diffusion probabilistic models. In Proceedings of the Advances in Neural Information Processing Systems (NeurIPS), volume 33, pp.  6840–6851, 2020.
  13. Debiasing scores and prompts of 2d diffusion for robust text-to-3d generation. ArXiv, abs/2303.15413, 2023.
  14. T2i-compbench: A comprehensive benchmark for open-world compositional text-to-image generation. arXiv preprint arXiv:2307.06350, 2023a.
  15. Dreamtime: An improved optimization strategy for text-to-3d content creation. arXiv preprint arXiv:2306.12422, 2023b.
  16. Zero-shot text-guided object generation with dream fields. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.  867–876, 2022.
  17. Instruct 3d-to-3d: Text instruction guided 3d-to-3d conversion. arXiv preprint arXiv:2303.15780, 2023.
  18. Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In International Conference on Machine Learning, pp.  12888–12900, 2022.
  19. Focaldreamer: Text-driven 3d editing via focal-fusion assembly. arXiv preprint arXiv:2308.10608, 2023.
  20. Magic3d: High-resolution text-to-3d content creation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.  300–309, 2023.
  21. Llmscore: Unveiling the power of large language models in text-to-image synthesis evaluation. arXiv preprint arXiv:2305.11116, 2023.
  22. Latent-nerf for shape-guided generation of 3d shapes and textures. arXiv preprint arXiv:2211.07600, 2022.
  23. Text2mesh: Text-driven neural stylization for meshes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp.  13492–13502, June 2022.
  24. Nerf: Representing scenes as neural radiance fields for view synthesis. In Proceedings of the European Conference on Computer Vision (ECCV), pp.  405–421, 2020.
  25. Clip-mesh: Generating textured meshes from text using pretrained image-text models. In SIGGRAPH Asia 2022 conference papers, pp.  1–8, 2022.
  26. T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models. ArXiv, abs/2302.08453, 2023.
  27. Glide: Towards photorealistic image generation and editing with text-guided diffusion models. In International Conference on Machine Learning, 2021.
  28. Dreamfusion: Text-to-3d using 2d diffusion. arXiv preprint arXiv:2209.14988, 2022.
  29. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, 2021.
  30. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.  10684–10695, 2022.
  31. Vox-e: Text-guided voxel editing of 3d objects. arXiv preprint arXiv:2303.12048, 2023.
  32. Deep marching tetrahedra: a hybrid representation for high-resolution 3d shape synthesis. Advances in Neural Information Processing Systems, 34:6087–6101, 2021.
  33. Deep unsupervised learning using nonequilibrium thermodynamics. arXiv: Learning,arXiv: Learning, 2015.
  34. Textmesh: Generation of realistic 3d meshes from text prompts. arXiv preprint arXiv:2304.12439, 2023.
  35. Score jacobian chaining: Lifting pretrained 2d diffusion models for 3d generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.  12619–12629, 2023a.
  36. Neus: Learning neural implicit surfaces by volume rendering for multi-view reconstruction. arXiv preprint arXiv:2106.10689, 2021.
  37. Prolificdreamer: High-fidelity and diverse text-to-3d generation with variational score distillation. arXiv preprint arXiv:2305.16213, 2023b.
  38. Volume rendering of neural implicit surfaces. Advances in Neural Information Processing Systems, 34:4805–4815, 2021.
  39. Adding conditional control to text-to-image diffusion models. In IEEE International Conference on Computer Vision (ICCV), 2023.
  40. Minigpt-4: Enhancing vision-language understanding with advanced large language models. arXiv preprint arXiv:2304.10592, 2023.
  41. Dreameditor: Text-driven 3d scene editing with neural fields. arXiv preprint arXiv:2306.13455, 2023.
Citations (31)
View on Semantic Scholar

Summary

  • The paper presents a progressive local editing framework that deconstructs complex semantic text prompts into sequential region-specific modifications.
  • It introduces the Overlapped Semantic Component Suppression technique to enhance precision by focusing on semantic differences during localized edits.
  • Experiments demonstrate that Progressive3D outperforms baseline models in achieving higher semantic alignment and improved user preference scores.

Essay on "Progressive3D: Progressively Local Editing for Text-to-3D Content Creation with Complex Semantic Prompts"

The paper "Progressive3D" by Xinhua Cheng et al. presents a novel framework aimed at enhancing the generation of 3D content from complex semantic text prompts. This work addresses significant challenges in the field of text-to-3D content creation, especially with prompts that involve multiple objects and intricate semantic descriptions.

The proposed framework, Progressive3D, introduces a systematic approach to decompose complex 3D content generation into a series of progressive local editing steps. The authors highlight the limitations of existing methods that struggle to maintain semantic consistency when dealing with complex prompts. Progressive3D constrains content modifications to only specified regions, focusing on semantic differences guided by user-defined prompts in each editing phase.

Key Innovations and Methodological Advancements

  1. Progressive Editing Framework: The paper introduces a method to iteratively build complex 3D scenes by editing a base model through multiple localized editing operations. This progressive approach allows for addressing semantic consistencies incrementally, facilitating a more precise alignment of generated content with intricate prompts.
  2. Region-Specific Constraints: Progressive3D uses user-defined region prompts to ensure changes are limited to desired areas without affecting the rest of the 3D scene. This selective modification is crucial for complex scenes where maintaining certain characteristics of the primary object or environment is essential.
  3. Overlapped Semantic Component Suppression (OSCS): The OSCS technique is a significant contribution, enabling the framework to focus on differences rather than redundancies between source and target prompts. This helps in achieving detailed and specific adjustments, reducing issues like attribute mismatching common in complex prompt scenarios.
  4. Versatility Across 3D Representations: The framework demonstrates compatibility with various 3D neural representations, including those based on NeRF, SDF, and DMTet, proving its efficacy across a wide range of existing methods. This adaptability enhances its applicability in diverse 3D content creation scenarios.

Empirical Evaluation

The experimental evaluation uses CSP-100, a complex semantic prompt set specifically designed to test the proposed methods. Results demonstrate that Progressive3D significantly improves upon existing text-to-3D techniques by achieving better semantic alignment in generated content. Numerical comparisons using metrics like BLIP-VQA and mGPT-CoT exhibit noticeable improvements, with the framework consistently outperforming baseline methods in user preference studies.

Theoretical and Practical Implications

The theoretical implications of this work lie in its novel approach to semantic isolation and enhancement, pushing forward the capabilities of autocreative systems in handling intricate instructions. Practically, Progressive3D presents a robust solution for industries reliant on high-fidelity 3D content, such as entertainment and virtual reality, enabling more intuitive and precise content creation workflows.

Future Directions

Looking ahead, there are intriguing prospects for further developing Progressive3D. Enhanced interaction strategies for defining user prompts, automation in region definition, and integration with real-time processing pipelines could expand its utility and performance. Additionally, exploring this framework's application with next-generation 3D representations could open new avenues for achieving even greater semantic fidelity and operational efficiency.

In conclusion, Progressive3D represents a substantial advancement in the domain of text-guided 3D content creation. By tackling the complexities of semantic detail and offering a structured editing approach, it sets a new benchmark for precision and applicability in the generation of three-dimensional digital artifacts.

File Document Download Save Streamline Icon: https://streamlinehq.com Markdown

Paper to Video (Beta)

Whiteboard

No one has generated a whiteboard explanation for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Sign Up to Generate

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Generate Now

Collections

Sign up for free to add this paper to one or more collections.

User Circle Single Streamline Icon: https://streamlinehq.com Sign Up