GaussianAnything transforms single images into editable 3D models using point cloud latent spaces.
Bridges the gap between 2D inputs and high-quality 3D generation.
It uses a point cloud-structured latent space amulti-view RGB-D-Normal renderings as input, enabling high-quality, interactive 3D generation from text or single images.
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https://arxiv.org/abs/2411.08033
🎯 Original Problem:
→ Current 3D generation methods struggle with three major challenges: limited input formats, inefficient latent space design, and suboptimal output representations.
→ Existing methods either use point clouds that miss texture details or multi-view images that lackrect 3D information.
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🔧 Solution in this Paper:
→ The framework employs a Variational Autoencoder with multi-view posed RGB-D-Normal renderings as input.
→ It introduces a point cloud-structured latent space that preserves 3D shape information through cross-attention mechanisms.
→ The system uses a cascaded latent diffusion model with flow matching for improved shape-texture separation.
→ It generates high-quality surfel Gaussians through an attention-based decoder for efficient rendering.
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💡 Key Insights:
→ Multi-view RGB-D-Normal input provides richer 3D information than traditional point clouds
→ Point cloud-structured latent space enables direct 3D editing and better geometry control
→ Cascaded diffusion approach improves shape-texture disentanglement
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📊 Results:
→ Achieves state-of-the-art performance on 3D metrics with P-FID of 8.72 and P-KID of 3.22%
→ Outperforms existing methods in both text and image-conditioned 3D generation
→ Shows superior performance in novel view synthesis and geometry reconstruction
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