The program ensures consistency in multi-angle generation through two core technologies:
1. Dual Appearance Module (DAM)
- Using a shared latent feature space to constrain the frontal and back feature mapping relationship
- Establishing semantic associations between perspectives through cross-attention mechanisms
- Maintaining Facial Structural Coherence Using Geometric Perceptual Loss Functions
2. ControlNet enhancements
- Reasoning about geometric hypotheses for invisible regions (e.g., the back of the head)
- Prediction of color distribution based on input image Backside material
- Progressive optimization of detail consistency through the denoising process of diffusion models
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Based on the previous work of PanoHead et al, the NeRF reconstruction accuracy is improved by 341 TP3T (thesis data), especially improving the naturalness of transition regions such as hairline and ear. Experiments show that the method maintains 87% structural consistency of the generated quality for extreme viewing angles (>150° deflection).
This answer comes from the articleDiffPortrait360: Generate 360-degree head views from a single portraitThe
May not be reproduced without permission:AI productivity tools " What are some of the technical breakthroughs that DiffPortrait360 has made in achieving view consistency?
