mlsanityData to 13 Oct 2026

EucliDreamer: Fast and High-Quality Texturing for 3D Models with Depth-Conditioned Stable Diffusion

arXiv 2404.10279 ↗First posted 16 Apr 2024cs.CVcited by 0

On the map: Diffusion models → 3D, point cloud and scene generation

Abstract (arXiv)

We present EucliDreamer, a simple and effective method to generate textures for 3D models given text prompts and meshes. The texture is parametrized as an implicit function on the 3D surface, which is optimized with the Score Distillation Sampling (SDS) process and differentiable rendering. To generate high-quality textures, we leverage a depth-conditioned Stable Diffusion model guided by the depth image rendered from the mesh. We test our approach on 3D models in Objaverse and conducted a user study, which shows its superior quality compared to existing texturing methods like Text2Tex. In addition, our method converges 2 times faster than DreamFusion. Through text prompting, textures of diverse art styles can be produced. We hope Euclidreamer proides a viable solution to automate a labor-intensive stage in 3D content creation.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Placed in 3D, point cloud and scene generation, one of 14 directions in Diffusion models. Builds on 21 of its 33 references that are in the map. First posted on arXiv 16 Apr 2024.

Builds on

21 of 33 references.

Closest ancestors · 4

Same direction, newest first

  1. 2023EucliDreamer: Fast and High-Quality Texturing for 3D Models with Stable Diffusion Depth27 Nov
  2. 2023ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation25 May
  3. 2023Text2Tex: Text-driven Texture Synthesis via Diffusion Models20 Mar
  4. 2022DreamFusion: Text-to-3D using 2D Diffusion29 Sep

Other references · 17

  1. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,777
  2. 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,484
  3. 2022Instant Neural Graphics Primitives with a Multiresolution Hash Encodingcited by 3,712
  4. 2020Accelerating 3D Deep Learning with PyTorch3Dcited by 784
  5. 2022Latent-NeRF for Shape-Guided Generation of 3D Shapes and Texturescited by 506
  6. 2020Modular Primitives for High-Performance Differentiable Renderingcited by 440
  7. 2022CLIP-Mesh: Generating textured meshes from text using pretrained image-text modelscited by 281
  8. 2018Total Capture: A 3D Deformation Model for Tracking Faces, Hands, and Bodiescited by 239
  9. 2023Re-imagine the Negative Prompt Algorithm: Transform 2D Diffusion into 3D, alleviate Janus problem and Beyondcited by 139
  10. 2021SynFace: Face Recognition with Synthetic Datacited by 106
  11. 2019Kaolin: A PyTorch Library for Accelerating 3D Deep Learning Researchcited by 98
  12. 2023Synthetic Datasets for Autonomous Driving: A Surveycited by 48
5 more
  1. 2017Region-based Quality Estimation Network for Large-scale Person Re-identificationcited by 33
  2. 2024Segment Anything Model for Road Network Graph Extractioncited by 28
  3. 2017Impression Network for Video Object Detectioncited by 13
  4. 2019Depth-wise Decomposition for Accelerating Separable Convolutions in Efficient Convolutional Neural Networkscited by 5
  5. 2023Novel View Synthesis from a Single RGBD Image for Indoor Scenescited by 4

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2023TEXTure: Text-Guided Texturing of 3D Shapes
  2. 2023TexFusion: Synthesizing 3D Textures with Text-Guided Image Diffusion Models
  3. 2024Tex4D: Zero-shot 4D Scene Texturing with Video Diffusion Models
  4. 2023Consistent Mesh Diffusion
  5. 2024TexPainter: Generative Mesh Texturing with Multi-view Consistency
  6. 2024StyleTex: Style Image-Guided Texture Generation for 3D Models
  7. 2024RoCoTex: A Robust Method for Consistent Texture Synthesis with Diffusion Models
  8. 2024MatAtlas: Text-driven Consistent Geometry Texturing and Material Assignment