How high-level semantic cues guide the diffusion process to differentiate between overlapping object boundaries.
Visual evidence of reduced noise and sharper depth transitions compared to state-of-the-art latent models. 4. Conclusion Pixelpiece3
This paper explores the transition from latent-space diffusion models to pixel-space diffusion generation . We address the "flying pixel" artifact—a common byproduct of Variational Autoencoder (VAE) compression—by performing diffusion directly in the pixel domain. By leveraging semantics-prompted diffusion , our approach ensures high-quality point cloud reconstruction from single-view images. 1. Introduction How high-level semantic cues guide the diffusion process
Detailed analysis of how bypassing latent-space compression removes "flying pixels" at depth discontinuities. 3. Quantitative and Qualitative Evaluation Pixelpiece3