Advances in 3D Vision and Rendering

Current Trends in 3D Vision and Rendering

Recent advancements in the field of 3D vision and rendering are pushing the boundaries of what is possible with multi-view consistency, real-time performance, and high-fidelity detail. The integration of diffusion models with 3D scene representations is a notable trend, enabling more robust and detailed 3D object and scene generation. This approach is particularly effective in preserving structural integrity across different viewpoints, as seen in methods that leverage diffusion models for style transfer and portrait generation. Additionally, there is a growing focus on efficient density control and optimization of rendering techniques, such as Gaussian Splatting, to enhance both speed and quality in novel view synthesis. These innovations are paving the way for more interactive and realistic 3D applications, from human-scene rendering to internal texture generation for 3D objects.

Noteworthy Developments

  • Multi-View Consistent Style Transfer: Leveraging diffusion models for multi-view style transfer, preserving structural integrity and reducing distortion across viewpoints.
  • Efficient Density Control in Gaussian Splatting: Enhancing rendering speed and quality by optimizing Gaussian utilization and reducing overlap.
  • High-Fidelity 3D Portrait Generation: Utilizing cross-view priors to generate detailed and consistent 3D portraits from single images.
  • Generalizable Human Reconstruction: Combining generalizable feed-forward models with diffusion priors for detailed 3D human reconstruction from sparse views.

Sources

Towards Multi-View Consistent Style Transfer with One-Step Diffusion via Vision Conditioning

Efficient Density Control for 3D Gaussian Splatting

Towards High-Fidelity 3D Portrait Generation with Rich Details by Cross-View Prior-Aware Diffusion

gDist: Efficient Distance Computation between 3D Meshes on GPU

GPS-Gaussian+: Generalizable Pixel-wise 3D Gaussian Splatting for Real-Time Human-Scene Rendering from Sparse Views

DiHuR: Diffusion-Guided Generalizable Human Reconstruction

FruitNinja: 3D Object Interior Texture Generation with Gaussian Splatting

Beyond Gaussians: Fast and High-Fidelity 3D Splatting with Linear Kernels

SplatR : Experience Goal Visual Rearrangement with 3D Gaussian Splatting and Dense Feature Matching

Baking Gaussian Splatting into Diffusion Denoiser for Fast and Scalable Single-stage Image-to-3D Generation

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