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SyncTweedies: A General Generative Framework Based on Synchronized Diffusions
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Proposes SyncTweedies, a framework that synchronizes multiple pretrained image diffusion processes through a canonical space by averaging Tweedie's-formula estimates, applied to ambiguous images, panoramas, 3D mesh textures, and 3DGS textures without fine-tuning.
Problem
Generating diverse visual content (ambiguous images, panoramas, 3D textures, arbitrary-sized images) that requires synchronizing multiple diffusion processes across canonical and instance spaces, where prior methods are optimization-based or require finetuning.
Approach
A general diffusion synchronization framework analyzing cases of canonical vs. instance variable denoising under 1-to-1, 1-to-n and n-to-1 mappings; the instance-space denoising averages Tweedie's formula outputs unprojected to a canonical space, reusing a pretrained image diffusion model without training.
Key results
- Reported 8-11x faster running time than optimization-based methods for 3D mesh texturing, and comparable or 3-7x faster than iterative-view-update methods (Section A5).
- Uses approximately 6-9 GiB of GPU memory on an NVIDIA RTX A6000, per rebuttal Table R1.
- Outperforms finetuned baselines Paint3D (3D mesh texturing) and MVDiffusion (depth-to-360 panorama) per the rebuttal, though exact metric values are not given in the text.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use SyncTweedies instead of optimization-based methods | 3D mesh texturing · 3D mesh texturing | texture quality | Helped |
| Use SyncTweedies instead of Paint-it | 3D mesh texturing · 3D mesh texturing | texture diversity | Helped |
| Use SyncTweedies instead of fine-tuned MVDiffusion | Depth-to-360 panorama generation · Depth-to-360 panorama | generation quality | Helped |
| Apply SyncTweedies to pretrained MVDiffusion | MVDiffusion · Qualitative comparison | generation quality | No clear effect |
| Use canonical-variable denoising with canonical-space noise initialization | SyncTweedies 3DGS texturing · 3DGS texturing | generation quality | Hurt |
| Use projected canonical noise initialization for instance-space denoising | SyncTweedies 3DGS texturing · 3DGS texturing | generation quality | Helped |
| Use any of Cases 1-5 rather than another synchronization case | SyncTweedies with 1-to-1 mappings · 1-to-1 projection toy experiment and arbitrary-sized image generation | generated result | No clear effect |
| Reduce the number of views | SyncTweedies · Visual generation applications | output quality | No clear effect |
Surprising findings
- Applying SyncTweedies on top of the pretrained MVDiffusion did not yield significant improvement, per the rebuttal.
Limitations
- Requires known geometry (projection/unprojection mappings), so it cannot jointly optimize geometry and texture.
- Multi-view noise prediction adds computational overhead; misaligned canonical and instance data distributions lack theoretical analysis.
- Canonical-space denoising cases suffer variance decrease when initializing noise from canonical space under n-to-1 mappings.
Experimental setup
Datasets: Synthetic NeRF dataset
Benchmarks: Synthetic NeRF dataset
Metrics: running time memory usage
Baselines: Paint-it Paint3D MVDiffusion DiffCollage
Builds on: MultiDiffusion SyncDiffusion Score Distillation Sampling Tweedie's formula
Closest papers by meaning
- StochSync: Stochastic Diffusion Synchronization for Image Generation in Arbitrary Spaces (ICLR 2025 · similarity 0.90)
- DiffSplat: Repurposing Image Diffusion Models for Scalable Gaussian Splat Generation (ICLR 2025 · similarity 0.87)
- SyncDreamer: Generating Multiview-consistent Images from a Single-view Image (ICLR 2024 · similarity 0.86)
- DreamTime: An Improved Optimization Strategy for Diffusion-Guided 3D Generation (ICLR 2024 · similarity 0.86)
- Taming Stable Diffusion for Text to 360 Panorama Image Generation (CVPR 2024 · similarity 0.86)
- Clockwork Diffusion: Efficient Generation With Model-Step Distillation (CVPR 2024 · similarity 0.86)
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