Core solution steps
The backside distortion mainly stems from the lack of backside feature data, and a dual optimization strategy is recommended:
- ControlNet enhancements: Enable inference.sh
--use_controlnetparameters, the module generates physically plausible dorsal structures for hair, ears, etc. by inferring geometric symmetry - Reference Chart Guidance: Place both the front view and 1-2 side and rear angle reference images (45°/90°) in the input_image/ directory, and the model will be feature fused by the dual appearance module
advanced skill
For special elements (glasses/hats):
- Use the project-supplied
accessory_mask.pyScript marking the accessory area - align
--texture_preservationParameters to 0.6-0.8 range to maintain material continuity - The generated results were analyzed using NeRFStudio's
--mesh_refinePerform local mesh reconstruction
Typical cases show that this method can improve the backside PSNR metric by 421 TP3T.
This answer comes from the articleDiffPortrait360: Generate 360-degree head views from a single portraitThe
May not be reproduced without permission:AI productivity tools " How to solve the problem of distorted backside details in the generated 360 degree view?
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